NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=hitrahr [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => hitrahr [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => hitrahr [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [1] => Array ( [:alias] => cc [:language] => en ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/category-functions.php [line] => 149 [function] => np_db_query [args] => Array ( [0] => hitrahr [1] => db_query [2] => SELECT entity_id FROM field_data_field_url_alias WHERE field_url_alias_value = :alias AND entity_type = 'taxonomy_term' AND language = :language [3] => Array ( [:alias] => cc [:language] => en ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/routes.logic.php [line] => 75 [function] => category_validate_url_alias [args] => Array ( [0] => cc ) ) [10] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 86 [function] => check_for_module [args] => Array ( [0] => /en/cc/matlabprescriptive [1] => Array ( [0] => [1] => cc [2] => matlabprescriptive [3] => en ) ) ) [11] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [12] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "mysql_identifier_quote_character" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "mysql_identifier_quote_character" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 397 [function] => variable_get [args] => Array ( [0] => mysql_identifier_quote_character [1] => ` ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 329 [function] => setPrefix [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [default] => ) ) ) [3] => Array ( [file] => /apps/hitra7/drupal7/includes/database/mysql/database.inc [line] => 349 [function] => __construct [class] => DatabaseConnection [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => mysql:host=localhost;port=3306;charset=utf8;dbname=common_fe [1] => root [2] => asdf [3] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) [4] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1796 [function] => __construct [class] => DatabaseConnection_mysql [object] => DatabaseConnection_mysql Object ( [target:protected] => [key:protected] => [logger:protected] => [transactionLayers:protected] => Array ( ) [driverClasses:protected] => Array ( ) [statementClass:protected] => DatabaseStatementBase [transactionSupport:protected] => 1 [transactionalDDLSupport:protected] => [temporaryNameIndex:protected] => 0 [connection:protected] => [connectionOptions:protected] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) ) [schema:protected] => [prefixes:protected] => Array ( [default] => ) [prefixSearch:protected] => Array ( [0] => { [1] => } ) [prefixReplace:protected] => Array ( [0] => [1] => ) [escapedNames:protected] => Array ( ) [escapedAliases:protected] => Array ( ) [unprefixedTablesMap:protected] => Array ( ) [needsCleanup:protected] => [reservedKeyWords:DatabaseConnection_mysql:private] => Array ( [0] => accessible [1] => add [2] => admin [3] => all [4] => alter [5] => analyze [6] => and [7] => as [8] => asc [9] => asensitive [10] => before [11] => between [12] => bigint [13] => binary [14] => blob [15] => both [16] => by [17] => call [18] => cascade [19] => case [20] => change [21] => char [22] => character [23] => check [24] => collate [25] => column [26] => condition [27] => constraint [28] => continue [29] => convert [30] => create [31] => cross [32] => cube [33] => cume_dist [34] => current_date [35] => current_time [36] => current_timestamp [37] => current_user [38] => cursor [39] => database [40] => databases [41] => day_hour [42] => day_microsecond [43] => day_minute [44] => day_second [45] => dec [46] => decimal [47] => declare [48] => default [49] => delayed [50] => delete [51] => dense_rank [52] => desc [53] => describe [54] => deterministic [55] => distinct [56] => distinctrow [57] => div [58] => double [59] => drop [60] => dual [61] => each [62] => else [63] => elseif [64] => empty [65] => enclosed [66] => escaped [67] => except [68] => exists [69] => exit [70] => explain [71] => false [72] => fetch [73] => first_value [74] => float [75] => float4 [76] => float8 [77] => for [78] => force [79] => foreign [80] => from [81] => fulltext [82] => function [83] => generated [84] => get [85] => grant [86] => group [87] => grouping [88] => groups [89] => having [90] => high_priority [91] => hour_microsecond [92] => hour_minute [93] => hour_second [94] => if [95] => ignore [96] => in [97] => index [98] => infile [99] => inner [100] => inout [101] => insensitive [102] => insert [103] => int [104] => int1 [105] => int2 [106] => int3 [107] => int4 [108] => int8 [109] => integer [110] => intersect [111] => interval [112] => into [113] => io_after_gtids [114] => io_before_gtids [115] => is [116] => iterate [117] => join [118] => json_table [119] => key [120] => keys [121] => kill [122] => lag [123] => last_value [124] => lateral [125] => lead [126] => leading [127] => leave [128] => left [129] => like [130] => limit [131] => linear [132] => lines [133] => load [134] => localtime [135] => localtimestamp [136] => lock [137] => long [138] => longblob [139] => longtext [140] => loop [141] => low_priority [142] => master_bind [143] => master_ssl_verify_server_cert [144] => match [145] => maxvalue [146] => mediumblob [147] => mediumint [148] => mediumtext [149] => middleint [150] => minute_microsecond [151] => minute_second [152] => mod [153] => modifies [154] => natural [155] => not [156] => no_write_to_binlog [157] => nth_value [158] => ntile [159] => null [160] => numeric [161] => of [162] => on [163] => optimize [164] => optimizer_costs [165] => option [166] => optionally [167] => or [168] => order [169] => out [170] => outer [171] => outfile [172] => over [173] => partition [174] => percent_rank [175] => persist [176] => persist_only [177] => precision [178] => primary [179] => procedure [180] => purge [181] => range [182] => rank [183] => read [184] => reads [185] => read_write [186] => real [187] => recursive [188] => references [189] => regexp [190] => release [191] => rename [192] => repeat [193] => replace [194] => require [195] => resignal [196] => restrict [197] => return [198] => revoke [199] => right [200] => rlike [201] => row [202] => rows [203] => row_number [204] => schema [205] => schemas [206] => second_microsecond [207] => select [208] => sensitive [209] => separator [210] => set [211] => show [212] => signal [213] => smallint [214] => spatial [215] => specific [216] => sql [217] => sqlexception [218] => sqlstate [219] => sqlwarning [220] => sql_big_result [221] => sql_calc_found_rows [222] => sql_small_result [223] => ssl [224] => starting [225] => stored [226] => straight_join [227] => system [228] => table [229] => terminated [230] => then [231] => tinyblob [232] => tinyint [233] => tinytext [234] => to [235] => trailing [236] => trigger [237] => true [238] => undo [239] => union [240] => unique [241] => unlock [242] => unsigned [243] => update [244] => usage [245] => use [246] => using [247] => utc_date [248] => utc_time [249] => utc_timestamp [250] => values [251] => varbinary [252] => varchar [253] => varcharacter [254] => varying [255] => virtual [256] => when [257] => where [258] => while [259] => window [260] => with [261] => write [262] => xor [263] => year_month [264] => zerofill ) ) [type] => -> [args] => Array ( [0] => Array ( [driver] => mysql [database] => common_fe [username] => root [password] => asdf [host] => localhost [prefix] => Array ( [default] => ) [pdo] => Array ( [1000] => 1 [20] => 1 [17] => 1 [1013] => ) ) ) ) [5] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 1582 [function] => openConnection [class] => Database [type] => :: [args] => Array ( [0] => common_fe [1] => default ) ) [6] => Array ( [file] => /apps/hitra7/drupal7/includes/database/database.inc [line] => 2467 [function] => getConnection [class] => Database [type] => :: [args] => Array ( [0] => default ) ) [7] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 31 [function] => db_query [args] => Array ( [0] => SELECT * FROM price_formulas WHERE country_code = :country_code [1] => Array ( [:country_code] => cr ) ) ) [8] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 111 [function] => np_db_query [args] => Array ( [0] => common_fe [1] => db_query [2] => SELECT * FROM price_formulas WHERE country_code = :country_code [3] => Array ( [:country_code] => cr ) ) ) [9] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 93 [function] => get_formula [args] => Array ( [0] => cr ) ) [10] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 355 [function] => course_price_v2_formula [args] => Array ( ) ) [11] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 344 [function] => course_price_change_to_fe_p [args] => Array ( [0] => matlabprescriptive [1] => 14 [2] => uk_premium,ca_high,za_midrange,pl_1500 [3] => 0 [4] => [5] => USD ) ) [12] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 316 [function] => course_price_get_default_price [args] => Array ( [0] => matlabprescriptive ) ) [13] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 15 [function] => course_price_get_price [args] => Array ( [0] => matlabprescriptive ) ) [14] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => matlabprescriptive ) ) [15] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [16] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [17] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [18] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "sdp" /apps/nobleprog-website/includes/functions/course-prices.php:281 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 281 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "sdp" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 281 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => matlabprescriptive [venue_id] => cr_10638141 [vfdc] => 150.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => matlabprescriptive ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "nobleprog_default_trainer_journey" /apps/nobleprog-website/includes/functions/course-prices.php:286 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 286 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_default_trainer_journey" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 286 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => matlabprescriptive [venue_id] => cr_10638141 [vfdc] => 150.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => matlabprescriptive ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "nobleprog_price_rounding" /apps/nobleprog-website/includes/functions/course-prices.php:289 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 289 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_price_rounding" [2] => /apps/nobleprog-website/includes/functions/course-prices.php [3] => 289 ) ) [1] => Array ( [file] => /apps/nobleprog-website/includes/functions/course-prices.php [line] => 45 [function] => course_price_table [args] => Array ( [0] => Array ( [fdp] => 5437 [adp] => 937 [reduced_fdp] => [reduced_adp] => [days] => 2 [default_venue_fdc] => 350 [default_venue_adc] => 50 [people] => 1 [hours] => 14 [course_code] => matlabprescriptive [venue_id] => cr_10638141 [vfdc] => 150.00 [vadc] => 50.00 ) [1] => 10 ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 23 [function] => course_price_virtual_event_price [args] => Array ( [0] => matlabprescriptive ) ) [3] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [4] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [6] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) /apps/nobleprog-website/modules/course/course.php:119 Array ( [0] => Array ( [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Cannot modify header information - headers already sent by (output started at /apps/nobleprog-website/_index.php:16) [2] => /apps/nobleprog-website/modules/course/course.php [3] => 119 ) ) [1] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 119 [function] => header [args] => Array ( [0] => X-CSRF-Token:Tm9ibGVQcm9nMTcxNjAyMzk0Nw== ) ) [2] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 82 [function] => course_generate_csrf_token [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => matlabprescriptive [hr_nid] => 213028 [title] => Matlab for Prescriptive Analytics [requirements] => [overview] =>

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Prescriptive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 838 [alias] => matlab-training [name] => MATLAB [english_name] => MATLAB [consulting_option] => available ) [1] => stdClass Object ( [tid] => 2276 [alias] => prescriptive-analytics-training [name] => Prescriptive Analytics [english_name] => Prescriptive Analytics [consulting_option] => available ) ) [2] => matlabprescriptive [3] => Array ( [outlines] => Array ( [bpmatlab] => stdClass Object ( [course_code] => bpmatlab [hr_nid] => 134913 [title] => Basic MATLAB Programming [requirements] =>

Basic programming knowledge recommended

[overview] =>

A 3 day course that takes you through the MATLAB main screens and windows including ...

[category_overview] => [outline] =>

Day 1

Day 2

Day 3

[language] => en [duration] => 21 [status] => published [changed] => 1700037204 [source_title] => Basic MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => bpmatlab ) [ipmat1] => stdClass Object ( [course_code] => ipmat1 [hr_nid] => 145969 [title] => Introduction to Image Processing using Matlab [requirements] =>

Basic knowledge of computer programming and images.

[overview] =>

This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images.

Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.

[category_overview] => [outline] =>

Day 1:

Day 2:

Day 3:

Day 4

[language] => en [duration] => 28 [status] => published [changed] => 1700037218 [source_title] => Introduction to Image Processing using Matlab [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => ipmat1 ) [matfin] => stdClass Object ( [course_code] => matfin [hr_nid] => 208205 [title] => MATLAB for Financial Applications [requirements] =>

A-level maths or economics, or relevant experience in the workplace, is advisable for this material

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>

Part I – Matlab Fundamentals

Matlab Basics

Matlab Programming

Working with Financial Data

Part II – Financial Applications

Overview of Matlab toolboxes relevant to Financial Analysis

Financial modelling basics

Regression and volatility

Portfolio theory and asset allocation

Asset pricing models

Market risk management

Optimization methods

[language] => en [duration] => 21 [status] => published [changed] => 1700037302 [source_title] => MATLAB for Financial Applications [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitraae,hitraza,hitrabr,hitraca,hitracn,hitrade,hitraeu,hitrafr,hitrahk,hitrasg,hitrahu,hitrain,hitrait,hitramx,hitranl,hitrapl,hitraro,hitraus,hitraes,hitrase,hitraph,hitratw [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matfin ) [matlab2] => stdClass Object ( [course_code] => matlab2 [hr_nid] => 279162 [title] => MATLAB Fundamentals [requirements] => [overview] =>

This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Va​riables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Conclusion

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Objectives: Summarise what we have learnt

Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.

[language] => en [duration] => 21 [status] => published [changed] => 1715349940 [source_title] => MATLAB Fundamentals [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlab2 ) [matlabdl] => stdClass Object ( [course_code] => matlabdl [hr_nid] => 212844 [title] => Matlab for Deep Learning [requirements] => [overview] =>

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Deep Learning [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdl ) [matlabdsandreporting] => stdClass Object ( [course_code] => matlabdsandreporting [hr_nid] => 204105 [title] => MATLAB Fundamentals, Data Science & Report Generation [requirements] =>

Audience

[overview] =>

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.

Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

Note

[category_overview] => [outline] =>

Introduction
MATLAB for data science and reporting

 

Part 01: MATLAB Fundamentals

Overview

Working with the MATLAB user interface

Overview of MATLAB syntax

Entering commands

Creating variables

Analyzing vectors and matrices

Visualizing vector and matrix data

Working with data files

Working with data types

Automating commands with scripts

Writing programs with branching and loops

Writing functions

Applying object-oriented programming principles to your programs

 

Part 02: MATLAB for Data Science

Overview

Accessing data

Exploring data

Creating customized algorithms

Creating visualizations

Creating models

Publishing customized reports

Sharing analysis tools

Using the Statistics and Machine Learning Toolbox

Using the Neural Network Toolbox

 

Part 03: Report Generation

Overview

Creating reports interactively vs programmatically

Creating reports interactively using Report Explorer

Creating reports programmatically in MATLAB


Summary and Closing Remarks

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals, Data Science & Report Generation [source_language] => en [cert_code] => [weight] => -996 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdsandreporting ) [matlabdynamicanalysis] => stdClass Object ( [course_code] => matlabdynamicanalysis [hr_nid] => 440063 [title] => Dynamic Analysis Using Matlab [requirements] =>

Audience

[overview] =>

Dynamic analysis is the process of testing and evaluating a material or program while running a software.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Matlab

Calculation of Natural Values Using Matlab

Dynamic Analysis of Material

Motion Analysis of Material

Vibration Analysis

Governing Equation of Motion

Closed-Form Analysis

Matlab Programming on Analytical Solutions

Numerical Analysis

Matlab Programming on Numerical Solutions

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1700037948 [source_title] => Dynamic Analysis Using Matlab [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdynamicanalysis ) [matlabfincance] => stdClass Object ( [course_code] => matlabfincance [hr_nid] => 205333 [title] => Matlab for Finance [requirements] =>

Course options

[overview] =>

MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics.

This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice.

By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems.

Audience

Format of the course

[category_overview] => [outline] =>

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1715349844 [source_title] => Matlab for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfincance ) [matlabfundamentalsfinance] => stdClass Object ( [course_code] => matlabfundamentalsfinance [hr_nid] => 205417 [title] => MATLAB Fundamentals + MATLAB for Finance [requirements] => [overview] =>

This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Variables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Part 4

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Part 5

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

Objectives: Summarise what we have learned

Note: the actual content delivered might differ from the outline as a result of customer requirements and the time spent on each topic.

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals + MATLAB for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfundamentalsfinance ) [matlabml1] => stdClass Object ( [course_code] => matlabml1 [hr_nid] => 97759 [title] => MATLAB与机器学习入门 [requirements] =>

Good math skills
Linear Algebra

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>
  1. MATLAB Basics
  2. MATLAB More Advanced Features
  3. BP Neural Network
  4. RBF, GRNN and PNN Neural Networks
  5. SOM Neural Networks
  6. Support Vector Machine, SVM
  7. Extreme Learning Machine, ELM
  8. Decision Trees and Random Forests
  9. Genetic Algorithm, GA
  10. Particle Swarm Optimization, PSO
  11. Ant Colony Algorithm, ACA
  12. Simulated Annealing, SA
  13. Dimenationality Reduction and Feature Selection
[language] => en [duration] => 21 [status] => published [changed] => 1715592376 [source_title] => MATLAB与机器学习入门 [source_language] => zh-hans [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabml1 ) [matlabpredanalytics] => stdClass Object ( [course_code] => matlabpredanalytics [hr_nid] => 212828 [title] => Matlab for Predictive Analytics [requirements] => [overview] =>

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

Introduction

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

[language] => en [duration] => 21 [status] => published [changed] => 1715349914 [source_title] => Matlab for Predictive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabpredanalytics ) [matlabprog] => stdClass Object ( [course_code] => matlabprog [hr_nid] => 2842 [title] => MATLAB Programming [requirements] => [overview] =>

This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

[category_overview] => [outline] => [language] => en [duration] => 14 [status] => published [changed] => 1700037086 [source_title] => MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabprog ) [octnp] => stdClass Object ( [course_code] => octnp [hr_nid] => 202281 [title] => Octave not only for programmers [requirements] => [overview] =>

Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.

[category_overview] => [outline] =>

Introduction

Simple calculations

The Octave environment

Arrays and vectors

Plotting graphs

Octave programming I: Script files

Control statements

Octave programming II: Functions

Matrices and vectors

Linear and Nonlinear Equations

More graphs

 Eigenvectors and the Singular Value Decomposition

 Complex numbers

 Statistics and data processing

 GUI Development

[language] => en [duration] => 21 [status] => published [changed] => 1700037275 [source_title] => Octave not only for programmers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => octnp ) [pythonformatlab] => stdClass Object ( [course_code] => pythonformatlab [hr_nid] => 306994 [title] => Python for Matlab Users [requirements] =>

Audience

[overview] =>

The Python programming language is becoming more and more popular among Matlab users due to its power and versatility as a data analysis tool as well as a general purpose language.

This instructor-led, live training (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Python Development Environment for Data Science

The Power of Matlab for Numerical Problem Solving

Python Libraries and Packages for Numerical Problem Solving and Data Analysis

Hands-on Practice with Python Syntax

Importing Data into Python

Matrix Manipulation

Math Operations

Visualizing Data

Converting an Existing Matlab Application to Python

Common Pitfalls when Transitioning to Python

Calling Matlab from within Python and Vice Versa

Python Wrappers for Providing a Matlab-like Interface

Summary and Conclusion

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Participants should have basic knowledge about Simulink

[overview] =>

Simulink is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems.

[category_overview] => [outline] =>
  1. Conditionally executed subsystems
  2. Enabled subsystems
  3. Triggered subsystems
  4. Input validation model

Create a simple Simulink model, simulate it, and analyze the results.

  1. Define the potentiometer system
  2. Explore the Simulink environment interface
  3. Create a Simulink model of the potentiometer system
  4. Simulate the model and analyze results
  1. Comparisons and decision statements
  2. Zero crossings
  3. MATLAB Function block

Modeling Discrete Systems Objective:

Model and simulate discrete systems in Simulink.

  1. Define discrete states
  2. Create a model of a PI controller
  3. Model discrete transfer functions and state space systems
  4. Model multirate discrete systems

Modeling Continuous Systems:

Model and simulate continuous systems in Simulink.

  1. Create a model of a throttle system
  2. Define continuous states
  3. Run simulations and analyze results
  4. Model impact dynamics

Solver Selection: Select a solver that is appropriate for a given Simulink model.

  1. Solver behavior
  2. System dynamics
  3. Discontinuities
  4. Algebraic loops
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[body] => The many examples and the building of the code from start to finish. [mc] => [is_mt] => 0 [nid] => 469183 ) ) [7] => 5 [8] => 1 [9] => 1 [10] => ) ) [4] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [5] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [6] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [7] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) Matlab for Prescriptive Analytics Training Course

Course Outline

To request a customized course outline for this training, please contact us.

Requirements

  • Experience with Matlab
 14 Hours

Number of participants



Price per participant

Open Training Courses require 5+ participants.

Testimonials (1)

The many examples and the building of the code from start to finish.

Toon - Draka Comteq Fibre B.V.
Course - Introduction to Image Processing using Matlab

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Prescriptive Analytics
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Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

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Basic programming knowledge recommended

[overview] =>

A 3 day course that takes you through the MATLAB main screens and windows including ...

[category_overview] => [outline] =>

Day 1

Day 2

Day 3

[language] => en [duration] => 21 [status] => published [changed] => 1700037204 [source_title] => Basic MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => bpmatlab ) [ipmat1] => stdClass Object ( [course_code] => ipmat1 [hr_nid] => 145969 [title] => Introduction to Image Processing using Matlab [requirements] =>

Basic knowledge of computer programming and images.

[overview] =>

This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images.

Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.

[category_overview] => [outline] =>

Day 1:

Day 2:

Day 3:

Day 4

[language] => en [duration] => 28 [status] => published [changed] => 1700037218 [source_title] => Introduction to Image Processing using Matlab [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => ipmat1 ) [matfin] => stdClass Object ( [course_code] => matfin [hr_nid] => 208205 [title] => MATLAB for Financial Applications [requirements] =>

A-level maths or economics, or relevant experience in the workplace, is advisable for this material

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>

Part I – Matlab Fundamentals

Matlab Basics

Matlab Programming

Working with Financial Data

Part II – Financial Applications

Overview of Matlab toolboxes relevant to Financial Analysis

Financial modelling basics

Regression and volatility

Portfolio theory and asset allocation

Asset pricing models

Market risk management

Optimization methods

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This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Va​riables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Conclusion

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Objectives: Summarise what we have learnt

Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.

[language] => en [duration] => 21 [status] => published [changed] => 1715349940 [source_title] => MATLAB Fundamentals [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlab2 ) [matlabdl] => stdClass Object ( [course_code] => matlabdl [hr_nid] => 212844 [title] => Matlab for Deep Learning [requirements] => [overview] =>

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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Audience

Format of the course

[category_overview] => [outline] =>

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[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Deep Learning [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdl ) [matlabdsandreporting] => stdClass Object ( [course_code] => matlabdsandreporting [hr_nid] => 204105 [title] => MATLAB Fundamentals, Data Science & Report Generation [requirements] =>

Audience

[overview] =>

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

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Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

Note

[category_overview] => [outline] =>

Introduction
MATLAB for data science and reporting

 

Part 01: MATLAB Fundamentals

Overview

Working with the MATLAB user interface

Overview of MATLAB syntax

Entering commands

Creating variables

Analyzing vectors and matrices

Visualizing vector and matrix data

Working with data files

Working with data types

Automating commands with scripts

Writing programs with branching and loops

Writing functions

Applying object-oriented programming principles to your programs

 

Part 02: MATLAB for Data Science

Overview

Accessing data

Exploring data

Creating customized algorithms

Creating visualizations

Creating models

Publishing customized reports

Sharing analysis tools

Using the Statistics and Machine Learning Toolbox

Using the Neural Network Toolbox

 

Part 03: Report Generation

Overview

Creating reports interactively vs programmatically

Creating reports interactively using Report Explorer

Creating reports programmatically in MATLAB


Summary and Closing Remarks

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Audience

[overview] =>

Dynamic analysis is the process of testing and evaluating a material or program while running a software.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Matlab

Calculation of Natural Values Using Matlab

Dynamic Analysis of Material

Motion Analysis of Material

Vibration Analysis

Governing Equation of Motion

Closed-Form Analysis

Matlab Programming on Analytical Solutions

Numerical Analysis

Matlab Programming on Numerical Solutions

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1700037948 [source_title] => Dynamic Analysis Using Matlab [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdynamicanalysis ) [matlabfincance] => stdClass Object ( [course_code] => matlabfincance [hr_nid] => 205333 [title] => Matlab for Finance [requirements] =>

Course options

[overview] =>

MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics.

This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice.

By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems.

Audience

Format of the course

[category_overview] => [outline] =>

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1715349844 [source_title] => Matlab for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfincance ) [matlabfundamentalsfinance] => stdClass Object ( [course_code] => matlabfundamentalsfinance [hr_nid] => 205417 [title] => MATLAB Fundamentals + MATLAB for Finance [requirements] => [overview] =>

This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Variables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Part 4

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Part 5

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

Objectives: Summarise what we have learned

Note: the actual content delivered might differ from the outline as a result of customer requirements and the time spent on each topic.

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals + MATLAB for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfundamentalsfinance ) [matlabml1] => stdClass Object ( [course_code] => matlabml1 [hr_nid] => 97759 [title] => MATLAB与机器学习入门 [requirements] =>

Good math skills
Linear Algebra

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>
  1. MATLAB Basics
  2. MATLAB More Advanced Features
  3. BP Neural Network
  4. RBF, GRNN and PNN Neural Networks
  5. SOM Neural Networks
  6. Support Vector Machine, SVM
  7. Extreme Learning Machine, ELM
  8. Decision Trees and Random Forests
  9. Genetic Algorithm, GA
  10. Particle Swarm Optimization, PSO
  11. Ant Colony Algorithm, ACA
  12. Simulated Annealing, SA
  13. Dimenationality Reduction and Feature Selection
[language] => en [duration] => 21 [status] => published [changed] => 1715592376 [source_title] => MATLAB与机器学习入门 [source_language] => zh-hans [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabml1 ) [matlabpredanalytics] => stdClass Object ( [course_code] => matlabpredanalytics [hr_nid] => 212828 [title] => Matlab for Predictive Analytics [requirements] => [overview] =>

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

Introduction

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

[language] => en [duration] => 21 [status] => published [changed] => 1715349914 [source_title] => Matlab for Predictive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabpredanalytics ) [matlabprog] => stdClass Object ( [course_code] => matlabprog [hr_nid] => 2842 [title] => MATLAB Programming [requirements] => [overview] =>

This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

[category_overview] => [outline] => [language] => en [duration] => 14 [status] => published [changed] => 1700037086 [source_title] => MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabprog ) [octnp] => stdClass Object ( [course_code] => octnp [hr_nid] => 202281 [title] => Octave not only for programmers [requirements] => [overview] =>

Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.

[category_overview] => [outline] =>

Introduction

Simple calculations

The Octave environment

Arrays and vectors

Plotting graphs

Octave programming I: Script files

Control statements

Octave programming II: Functions

Matrices and vectors

Linear and Nonlinear Equations

More graphs

 Eigenvectors and the Singular Value Decomposition

 Complex numbers

 Statistics and data processing

 GUI Development

[language] => en [duration] => 21 [status] => published [changed] => 1700037275 [source_title] => Octave not only for programmers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => octnp ) [pythonformatlab] => stdClass Object ( [course_code] => pythonformatlab [hr_nid] => 306994 [title] => Python for Matlab Users [requirements] =>

Audience

[overview] =>

The Python programming language is becoming more and more popular among Matlab users due to its power and versatility as a data analysis tool as well as a general purpose language.

This instructor-led, live training (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Python Development Environment for Data Science

The Power of Matlab for Numerical Problem Solving

Python Libraries and Packages for Numerical Problem Solving and Data Analysis

Hands-on Practice with Python Syntax

Importing Data into Python

Matrix Manipulation

Math Operations

Visualizing Data

Converting an Existing Matlab Application to Python

Common Pitfalls when Transitioning to Python

Calling Matlab from within Python and Vice Versa

Python Wrappers for Providing a Matlab-like Interface

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037484 [source_title] => Python for Matlab Users [source_language] => en [cert_code] => [weight] => -977 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => pythonformatlab ) [simulinkadv] => stdClass Object ( [course_code] => simulinkadv [hr_nid] => 198633 [title] => Simulink® for Automotive System Design Advanced Level [requirements] =>

Participants should have basic knowledge about Simulink

[overview] =>

Simulink is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems.

[category_overview] => [outline] =>
  1. Conditionally executed subsystems
  2. Enabled subsystems
  3. Triggered subsystems
  4. Input validation model

Create a simple Simulink model, simulate it, and analyze the results.

  1. Define the potentiometer system
  2. Explore the Simulink environment interface
  3. Create a Simulink model of the potentiometer system
  4. Simulate the model and analyze results
  1. Comparisons and decision statements
  2. Zero crossings
  3. MATLAB Function block

Modeling Discrete Systems Objective:

Model and simulate discrete systems in Simulink.

  1. Define discrete states
  2. Create a model of a PI controller
  3. Model discrete transfer functions and state space systems
  4. Model multirate discrete systems

Modeling Continuous Systems:

Model and simulate continuous systems in Simulink.

  1. Create a model of a throttle system
  2. Define continuous states
  3. Run simulations and analyze results
  4. Model impact dynamics

Solver Selection: Select a solver that is appropriate for a given Simulink model.

  1. Solver behavior
  2. System dynamics
  3. Discontinuities
  4. Algebraic loops
[language] => en [duration] => 14 [status] => published [changed] => 1700037246 [source_title] => Simulink® for Automotive System Design Advanced Level [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => simulinkadv ) ) [codes] => Array ( [0] => bpmatlab [1] => ipmat1 [2] => matfin [3] => matlab2 [4] => matlabdl [5] => matlabdsandreporting [6] => matlabdynamicanalysis [7] => matlabfincance [8] => matlabfundamentalsfinance [9] => matlabml1 [10] => matlabpredanalytics [11] => matlabprog [12] => octnp [13] => pythonformatlab [14] => simulinkadv ) ) [4] => Array ( [regions] => Array ( [cr_1745] => Array ( [tid] => cr_1745 [title] => San José [sales_area] => cr_costa_rica [venues] => Array ( [cr_10638141] => Array ( [vid] => cr_10638141 [title] => San Jose - Plaza Roble Las Terrazas [vfdc] => 150.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 5437 [classroom guaranteed] => 5737 [remote guaranteed per delegate] => 5437 [delegates] => 1 [adp] => 937 [classroom guaranteed per delegate] => 5737 ) [2] => Array ( [remote guaranteed] => 6374 [classroom guaranteed] => 6774 [remote guaranteed per delegate] => 3187 [delegates] => 2 [adp] => 937 [classroom guaranteed per delegate] => 3387 ) [3] => Array ( [remote guaranteed] => 7311 [classroom guaranteed] => 7812 [remote guaranteed per delegate] => 2437 [delegates] => 3 [adp] => 937 [classroom guaranteed per delegate] => 2604 ) [4] => Array ( [remote guaranteed] => 8248 [classroom guaranteed] => 8848 [remote guaranteed per delegate] => 2062 [delegates] => 4 [adp] => 937 [classroom guaranteed per delegate] => 2212 ) [5] => Array ( [remote guaranteed] => 9185 [classroom guaranteed] => 9885 [remote guaranteed per delegate] => 1837 [delegates] => 5 [adp] => 937 [classroom guaranteed per delegate] => 1977 ) [6] => Array ( [remote guaranteed] => 10122 [classroom guaranteed] => 10920 [remote guaranteed per delegate] => 1687 [delegates] => 6 [adp] => 937 [classroom guaranteed per delegate] => 1820 ) [7] => Array ( [remote guaranteed] => 11060 [classroom guaranteed] => 11956 [remote guaranteed per delegate] => 1580 [delegates] => 7 [adp] => 937 [classroom guaranteed per delegate] => 1708 ) [8] => Array ( [remote guaranteed] => 12000 [classroom guaranteed] => 13000 [remote guaranteed per delegate] => 1500 [delegates] => 8 [adp] => 937 [classroom guaranteed per delegate] => 1625 ) [9] => Array ( [remote guaranteed] => 12933 [classroom guaranteed] => 14031 [remote guaranteed per delegate] => 1437 [delegates] => 9 [adp] => 937 [classroom guaranteed per delegate] => 1559 ) [10] => Array ( [remote guaranteed] => 13870 [classroom guaranteed] => 15070 [remote guaranteed per delegate] => 1387 [delegates] => 10 [adp] => 937 [classroom guaranteed per delegate] => 1507 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 5437 [remote guaranteed per delegate] => 5437 [adp] => 937 ) [2] => Array ( [remote guaranteed] => 6374 [remote guaranteed per delegate] => 3187 [adp] => 937 ) [3] => Array ( [remote guaranteed] => 7311 [remote guaranteed per delegate] => 2437 [adp] => 937 ) [4] => Array ( [remote guaranteed] => 8248 [remote guaranteed per delegate] => 2062 [adp] => 937 ) [5] => Array ( [remote guaranteed] => 9185 [remote guaranteed per delegate] => 1837 [adp] => 937 ) [6] => Array ( [remote guaranteed] => 10122 [remote guaranteed per delegate] => 1687 [adp] => 937 ) [7] => Array ( [remote guaranteed] => 11060 [remote guaranteed per delegate] => 1580 [adp] => 937 ) [8] => Array ( [remote guaranteed] => 12000 [remote guaranteed per delegate] => 1500 [adp] => 937 ) [9] => Array ( [remote guaranteed] => 12933 [remote guaranteed per delegate] => 1437 [adp] => 937 ) [10] => Array ( [remote guaranteed] => 13870 [remote guaranteed per delegate] => 1387 [adp] => 937 ) ) [currency] => USD ) [5] => Array ( [0] => 5 ) [6] => Array ( [469183] => Array ( [title] => Introduction to Image Processing using Matlab [rating] => 5 [delegate_and_company] => Toon - Draka Comteq Fibre B.V. [body] => The many examples and the building of the code from start to finish. [mc] => [is_mt] => 0 [nid] => 469183 ) ) [7] => 5 [8] => 1 [9] => 1 [10] => ) ) [7] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [8] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [9] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [10] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "devel_domain" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "devel_domain" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 99 [function] => variable_get [args] => Array ( [0] => devel_domain [1] => ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 34 [function] => islc_get_site_list [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => matlabprescriptive [hr_nid] => 213028 [title] => Matlab for Prescriptive Analytics [requirements] => [overview] =>

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Prescriptive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 838 [alias] => matlab-training [name] => MATLAB [english_name] => MATLAB [consulting_option] => available ) [1] => stdClass Object ( [tid] => 2276 [alias] => prescriptive-analytics-training [name] => Prescriptive Analytics [english_name] => Prescriptive Analytics [consulting_option] => available ) ) [2] => matlabprescriptive [3] => Array ( [outlines] => Array ( [bpmatlab] => stdClass Object ( [course_code] => bpmatlab [hr_nid] => 134913 [title] => Basic MATLAB Programming [requirements] =>

Basic programming knowledge recommended

[overview] =>

A 3 day course that takes you through the MATLAB main screens and windows including ...

[category_overview] => [outline] =>

Day 1

Day 2

Day 3

[language] => en [duration] => 21 [status] => published [changed] => 1700037204 [source_title] => Basic MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => bpmatlab ) [ipmat1] => stdClass Object ( [course_code] => ipmat1 [hr_nid] => 145969 [title] => Introduction to Image Processing using Matlab [requirements] =>

Basic knowledge of computer programming and images.

[overview] =>

This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images.

Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.

[category_overview] => [outline] =>

Day 1:

Day 2:

Day 3:

Day 4

[language] => en [duration] => 28 [status] => published [changed] => 1700037218 [source_title] => Introduction to Image Processing using Matlab [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => ipmat1 ) [matfin] => stdClass Object ( [course_code] => matfin [hr_nid] => 208205 [title] => MATLAB for Financial Applications [requirements] =>

A-level maths or economics, or relevant experience in the workplace, is advisable for this material

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>

Part I – Matlab Fundamentals

Matlab Basics

Matlab Programming

Working with Financial Data

Part II – Financial Applications

Overview of Matlab toolboxes relevant to Financial Analysis

Financial modelling basics

Regression and volatility

Portfolio theory and asset allocation

Asset pricing models

Market risk management

Optimization methods

[language] => en [duration] => 21 [status] => published [changed] => 1700037302 [source_title] => MATLAB for Financial Applications [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitraae,hitraza,hitrabr,hitraca,hitracn,hitrade,hitraeu,hitrafr,hitrahk,hitrasg,hitrahu,hitrain,hitrait,hitramx,hitranl,hitrapl,hitraro,hitraus,hitraes,hitrase,hitraph,hitratw [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matfin ) [matlab2] => stdClass Object ( [course_code] => matlab2 [hr_nid] => 279162 [title] => MATLAB Fundamentals [requirements] => [overview] =>

This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Va​riables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Conclusion

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Objectives: Summarise what we have learnt

Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.

[language] => en [duration] => 21 [status] => published [changed] => 1715349940 [source_title] => MATLAB Fundamentals [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlab2 ) [matlabdl] => stdClass Object ( [course_code] => matlabdl [hr_nid] => 212844 [title] => Matlab for Deep Learning [requirements] => [overview] =>

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Deep Learning [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdl ) [matlabdsandreporting] => stdClass Object ( [course_code] => matlabdsandreporting [hr_nid] => 204105 [title] => MATLAB Fundamentals, Data Science & Report Generation [requirements] =>

Audience

[overview] =>

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.

Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

Note

[category_overview] => [outline] =>

Introduction
MATLAB for data science and reporting

 

Part 01: MATLAB Fundamentals

Overview

Working with the MATLAB user interface

Overview of MATLAB syntax

Entering commands

Creating variables

Analyzing vectors and matrices

Visualizing vector and matrix data

Working with data files

Working with data types

Automating commands with scripts

Writing programs with branching and loops

Writing functions

Applying object-oriented programming principles to your programs

 

Part 02: MATLAB for Data Science

Overview

Accessing data

Exploring data

Creating customized algorithms

Creating visualizations

Creating models

Publishing customized reports

Sharing analysis tools

Using the Statistics and Machine Learning Toolbox

Using the Neural Network Toolbox

 

Part 03: Report Generation

Overview

Creating reports interactively vs programmatically

Creating reports interactively using Report Explorer

Creating reports programmatically in MATLAB


Summary and Closing Remarks

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals, Data Science & Report Generation [source_language] => en [cert_code] => [weight] => -996 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdsandreporting ) [matlabdynamicanalysis] => stdClass Object ( [course_code] => matlabdynamicanalysis [hr_nid] => 440063 [title] => Dynamic Analysis Using Matlab [requirements] =>

Audience

[overview] =>

Dynamic analysis is the process of testing and evaluating a material or program while running a software.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Matlab

Calculation of Natural Values Using Matlab

Dynamic Analysis of Material

Motion Analysis of Material

Vibration Analysis

Governing Equation of Motion

Closed-Form Analysis

Matlab Programming on Analytical Solutions

Numerical Analysis

Matlab Programming on Numerical Solutions

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1700037948 [source_title] => Dynamic Analysis Using Matlab [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdynamicanalysis ) [matlabfincance] => stdClass Object ( [course_code] => matlabfincance [hr_nid] => 205333 [title] => Matlab for Finance [requirements] =>

Course options

[overview] =>

MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics.

This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice.

By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems.

Audience

Format of the course

[category_overview] => [outline] =>

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1715349844 [source_title] => Matlab for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfincance ) [matlabfundamentalsfinance] => stdClass Object ( [course_code] => matlabfundamentalsfinance [hr_nid] => 205417 [title] => MATLAB Fundamentals + MATLAB for Finance [requirements] => [overview] =>

This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Variables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Part 4

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Part 5

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

Objectives: Summarise what we have learned

Note: the actual content delivered might differ from the outline as a result of customer requirements and the time spent on each topic.

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals + MATLAB for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfundamentalsfinance ) [matlabml1] => stdClass Object ( [course_code] => matlabml1 [hr_nid] => 97759 [title] => MATLAB与机器学习入门 [requirements] =>

Good math skills
Linear Algebra

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>
  1. MATLAB Basics
  2. MATLAB More Advanced Features
  3. BP Neural Network
  4. RBF, GRNN and PNN Neural Networks
  5. SOM Neural Networks
  6. Support Vector Machine, SVM
  7. Extreme Learning Machine, ELM
  8. Decision Trees and Random Forests
  9. Genetic Algorithm, GA
  10. Particle Swarm Optimization, PSO
  11. Ant Colony Algorithm, ACA
  12. Simulated Annealing, SA
  13. Dimenationality Reduction and Feature Selection
[language] => en [duration] => 21 [status] => published [changed] => 1715592376 [source_title] => MATLAB与机器学习入门 [source_language] => zh-hans [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabml1 ) [matlabpredanalytics] => stdClass Object ( [course_code] => matlabpredanalytics [hr_nid] => 212828 [title] => Matlab for Predictive Analytics [requirements] => [overview] =>

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

Introduction

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

[language] => en [duration] => 21 [status] => published [changed] => 1715349914 [source_title] => Matlab for Predictive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabpredanalytics ) [matlabprog] => stdClass Object ( [course_code] => matlabprog [hr_nid] => 2842 [title] => MATLAB Programming [requirements] => [overview] =>

This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

[category_overview] => [outline] => [language] => en [duration] => 14 [status] => published [changed] => 1700037086 [source_title] => MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabprog ) [octnp] => stdClass Object ( [course_code] => octnp [hr_nid] => 202281 [title] => Octave not only for programmers [requirements] => [overview] =>

Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.

[category_overview] => [outline] =>

Introduction

Simple calculations

The Octave environment

Arrays and vectors

Plotting graphs

Octave programming I: Script files

Control statements

Octave programming II: Functions

Matrices and vectors

Linear and Nonlinear Equations

More graphs

 Eigenvectors and the Singular Value Decomposition

 Complex numbers

 Statistics and data processing

 GUI Development

[language] => en [duration] => 21 [status] => published [changed] => 1700037275 [source_title] => Octave not only for programmers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => octnp ) [pythonformatlab] => stdClass Object ( [course_code] => pythonformatlab [hr_nid] => 306994 [title] => Python for Matlab Users [requirements] =>

Audience

[overview] =>

The Python programming language is becoming more and more popular among Matlab users due to its power and versatility as a data analysis tool as well as a general purpose language.

This instructor-led, live training (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Python Development Environment for Data Science

The Power of Matlab for Numerical Problem Solving

Python Libraries and Packages for Numerical Problem Solving and Data Analysis

Hands-on Practice with Python Syntax

Importing Data into Python

Matrix Manipulation

Math Operations

Visualizing Data

Converting an Existing Matlab Application to Python

Common Pitfalls when Transitioning to Python

Calling Matlab from within Python and Vice Versa

Python Wrappers for Providing a Matlab-like Interface

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037484 [source_title] => Python for Matlab Users [source_language] => en [cert_code] => [weight] => -977 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => pythonformatlab ) [simulinkadv] => stdClass Object ( [course_code] => simulinkadv [hr_nid] => 198633 [title] => Simulink® for Automotive System Design Advanced Level [requirements] =>

Participants should have basic knowledge about Simulink

[overview] =>

Simulink is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems.

[category_overview] => [outline] =>
  1. Conditionally executed subsystems
  2. Enabled subsystems
  3. Triggered subsystems
  4. Input validation model

Create a simple Simulink model, simulate it, and analyze the results.

  1. Define the potentiometer system
  2. Explore the Simulink environment interface
  3. Create a Simulink model of the potentiometer system
  4. Simulate the model and analyze results
  1. Comparisons and decision statements
  2. Zero crossings
  3. MATLAB Function block

Modeling Discrete Systems Objective:

Model and simulate discrete systems in Simulink.

  1. Define discrete states
  2. Create a model of a PI controller
  3. Model discrete transfer functions and state space systems
  4. Model multirate discrete systems

Modeling Continuous Systems:

Model and simulate continuous systems in Simulink.

  1. Create a model of a throttle system
  2. Define continuous states
  3. Run simulations and analyze results
  4. Model impact dynamics

Solver Selection: Select a solver that is appropriate for a given Simulink model.

  1. Solver behavior
  2. System dynamics
  3. Discontinuities
  4. Algebraic loops
[language] => en [duration] => 14 [status] => published [changed] => 1700037246 [source_title] => Simulink® for Automotive System Design Advanced Level [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => simulinkadv ) ) [codes] => Array ( [0] => bpmatlab [1] => ipmat1 [2] => matfin [3] => matlab2 [4] => matlabdl [5] => matlabdsandreporting [6] => matlabdynamicanalysis [7] => matlabfincance [8] => matlabfundamentalsfinance [9] => matlabml1 [10] => matlabpredanalytics [11] => matlabprog [12] => octnp [13] => pythonformatlab [14] => simulinkadv ) ) [4] => Array ( [regions] => Array ( [cr_1745] => Array ( [tid] => cr_1745 [title] => San José [sales_area] => cr_costa_rica [venues] => Array ( [cr_10638141] => Array ( [vid] => cr_10638141 [title] => San Jose - Plaza Roble Las Terrazas [vfdc] => 150.00 [prices] => Array ( [1] => Array ( [remote guaranteed] => 5437 [classroom guaranteed] => 5737 [remote guaranteed per delegate] => 5437 [delegates] => 1 [adp] => 937 [classroom guaranteed per delegate] => 5737 ) [2] => Array ( [remote guaranteed] => 6374 [classroom guaranteed] => 6774 [remote guaranteed per delegate] => 3187 [delegates] => 2 [adp] => 937 [classroom guaranteed per delegate] => 3387 ) [3] => Array ( [remote guaranteed] => 7311 [classroom guaranteed] => 7812 [remote guaranteed per delegate] => 2437 [delegates] => 3 [adp] => 937 [classroom guaranteed per delegate] => 2604 ) [4] => Array ( [remote guaranteed] => 8248 [classroom guaranteed] => 8848 [remote guaranteed per delegate] => 2062 [delegates] => 4 [adp] => 937 [classroom guaranteed per delegate] => 2212 ) [5] => Array ( [remote guaranteed] => 9185 [classroom guaranteed] => 9885 [remote guaranteed per delegate] => 1837 [delegates] => 5 [adp] => 937 [classroom guaranteed per delegate] => 1977 ) [6] => Array ( [remote guaranteed] => 10122 [classroom guaranteed] => 10920 [remote guaranteed per delegate] => 1687 [delegates] => 6 [adp] => 937 [classroom guaranteed per delegate] => 1820 ) [7] => Array ( [remote guaranteed] => 11060 [classroom guaranteed] => 11956 [remote guaranteed per delegate] => 1580 [delegates] => 7 [adp] => 937 [classroom guaranteed per delegate] => 1708 ) [8] => Array ( [remote guaranteed] => 12000 [classroom guaranteed] => 13000 [remote guaranteed per delegate] => 1500 [delegates] => 8 [adp] => 937 [classroom guaranteed per delegate] => 1625 ) [9] => Array ( [remote guaranteed] => 12933 [classroom guaranteed] => 14031 [remote guaranteed per delegate] => 1437 [delegates] => 9 [adp] => 937 [classroom guaranteed per delegate] => 1559 ) [10] => Array ( [remote guaranteed] => 13870 [classroom guaranteed] => 15070 [remote guaranteed per delegate] => 1387 [delegates] => 10 [adp] => 937 [classroom guaranteed per delegate] => 1507 ) ) ) ) ) ) [remote] => Array ( [1] => Array ( [remote guaranteed] => 5437 [remote guaranteed per delegate] => 5437 [adp] => 937 ) [2] => Array ( [remote guaranteed] => 6374 [remote guaranteed per delegate] => 3187 [adp] => 937 ) [3] => Array ( [remote guaranteed] => 7311 [remote guaranteed per delegate] => 2437 [adp] => 937 ) [4] => Array ( [remote guaranteed] => 8248 [remote guaranteed per delegate] => 2062 [adp] => 937 ) [5] => Array ( [remote guaranteed] => 9185 [remote guaranteed per delegate] => 1837 [adp] => 937 ) [6] => Array ( [remote guaranteed] => 10122 [remote guaranteed per delegate] => 1687 [adp] => 937 ) [7] => Array ( [remote guaranteed] => 11060 [remote guaranteed per delegate] => 1580 [adp] => 937 ) [8] => Array ( [remote guaranteed] => 12000 [remote guaranteed per delegate] => 1500 [adp] => 937 ) [9] => Array ( [remote guaranteed] => 12933 [remote guaranteed per delegate] => 1437 [adp] => 937 ) [10] => Array ( [remote guaranteed] => 13870 [remote guaranteed per delegate] => 1387 [adp] => 937 ) ) [currency] => USD ) [5] => Array ( [0] => 5 ) [6] => Array ( [469183] => Array ( [title] => Introduction to Image Processing using Matlab [rating] => 5 [delegate_and_company] => Toon - Draka Comteq Fibre B.V. [body] => The many examples and the building of the code from start to finish. [mc] => [is_mt] => 0 [nid] => 469183 ) ) [7] => 5 [8] => 1 [9] => 1 [10] => ) ) [6] => Array ( [file] => /apps/nobleprog-website/core/routes.php [line] => 19 [function] => course_menu_callback [args] => Array ( [0] => /en/cc/matlabprescriptive ) ) [7] => Array ( [file] => /apps/nobleprog-website/__index.php [line] => 100 [args] => Array ( [0] => /apps/nobleprog-website/core/routes.php ) [function] => require_once ) [8] => Array ( [file] => /apps/nobleprog-website/_index.php [line] => 26 [args] => Array ( [0] => /apps/nobleprog-website/__index.php ) [function] => include_once ) [9] => Array ( [file] => /apps/hitra7/index.php [line] => 54 [args] => Array ( [0] => /apps/nobleprog-website/_index.php ) [function] => include_once ) ) NP URI: www.nobleprog.co.cr/en/cc/matlabprescriptive Undefined array key "nobleprog_site_production_url" /apps/nobleprog-website/includes/functions/new-modules-general-functions.php:82 Array ( [0] => Array ( [file] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [line] => 82 [function] => myErrorHandler [args] => Array ( [0] => 2 [1] => Undefined array key "nobleprog_site_production_url" [2] => /apps/nobleprog-website/includes/functions/new-modules-general-functions.php [3] => 82 ) ) [1] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7.module [line] => 131 [function] => variable_get [args] => Array ( [0] => nobleprog_site_production_url ) ) [2] => Array ( [file] => /apps/hitra7/drupal7/sites/all/modules/_custom/frontend/islc7/islc7_block.inc [line] => 44 [function] => islc_get_current_site [args] => Array ( ) ) [3] => Array ( [file] => /apps/nobleprog-website/nptemplates/default.php [line] => 265 [function] => islc7_sites_links_array_v3 [args] => Array ( ) ) [4] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 85 [args] => Array ( [0] => /apps/nobleprog-website/nptemplates/default.php ) [function] => require_once ) [5] => Array ( [file] => /apps/nobleprog-website/modules/course/course.php [line] => 31 [function] => course_render [args] => Array ( [0] => Array ( [course_code] => matlabprescriptive [hr_nid] => 213028 [title] => Matlab for Prescriptive Analytics [requirements] => [overview] =>

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Prescriptive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) ) [1] => Array ( [0] => stdClass Object ( [tid] => 838 [alias] => matlab-training [name] => MATLAB [english_name] => MATLAB [consulting_option] => available ) [1] => stdClass Object ( [tid] => 2276 [alias] => prescriptive-analytics-training [name] => Prescriptive Analytics [english_name] => Prescriptive Analytics [consulting_option] => available ) ) [2] => matlabprescriptive [3] => Array ( [outlines] => Array ( [bpmatlab] => stdClass Object ( [course_code] => bpmatlab [hr_nid] => 134913 [title] => Basic MATLAB Programming [requirements] =>

Basic programming knowledge recommended

[overview] =>

A 3 day course that takes you through the MATLAB main screens and windows including ...

[category_overview] => [outline] =>

Day 1

Day 2

Day 3

[language] => en [duration] => 21 [status] => published [changed] => 1700037204 [source_title] => Basic MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => bpmatlab ) [ipmat1] => stdClass Object ( [course_code] => ipmat1 [hr_nid] => 145969 [title] => Introduction to Image Processing using Matlab [requirements] =>

Basic knowledge of computer programming and images.

[overview] =>

This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images.

Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process.

[category_overview] => [outline] =>

Day 1:

Day 2:

Day 3:

Day 4

[language] => en [duration] => 28 [status] => published [changed] => 1700037218 [source_title] => Introduction to Image Processing using Matlab [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => ipmat1 ) [matfin] => stdClass Object ( [course_code] => matfin [hr_nid] => 208205 [title] => MATLAB for Financial Applications [requirements] =>

A-level maths or economics, or relevant experience in the workplace, is advisable for this material

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>

Part I – Matlab Fundamentals

Matlab Basics

Matlab Programming

Working with Financial Data

Part II – Financial Applications

Overview of Matlab toolboxes relevant to Financial Analysis

Financial modelling basics

Regression and volatility

Portfolio theory and asset allocation

Asset pricing models

Market risk management

Optimization methods

[language] => en [duration] => 21 [status] => published [changed] => 1700037302 [source_title] => MATLAB for Financial Applications [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitraae,hitraza,hitrabr,hitraca,hitracn,hitrade,hitraeu,hitrafr,hitrahk,hitrasg,hitrahu,hitrain,hitrait,hitramx,hitranl,hitrapl,hitraro,hitraus,hitraes,hitrase,hitraph,hitratw [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matfin ) [matlab2] => stdClass Object ( [course_code] => matlab2 [hr_nid] => 279162 [title] => MATLAB Fundamentals [requirements] => [overview] =>

This three-day course provides a comprehensive introduction to the MATLAB technical computing environment. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Va​riables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Conclusion

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Objectives: Summarise what we have learnt

Note that the course might be subject to few minor discrepancies when being delivered without prior notifications.

[language] => en [duration] => 21 [status] => published [changed] => 1715349940 [source_title] => MATLAB Fundamentals [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlab2 ) [matlabdl] => stdClass Object ( [course_code] => matlabdl [hr_nid] => 212844 [title] => Matlab for Deep Learning [requirements] => [overview] =>

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

To request a customized course outline for this training, please contact us.

[language] => en [duration] => 14 [status] => published [changed] => 1715349914 [source_title] => Matlab for Deep Learning [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdl ) [matlabdsandreporting] => stdClass Object ( [course_code] => matlabdsandreporting [hr_nid] => 204105 [title] => MATLAB Fundamentals, Data Science & Report Generation [requirements] =>

Audience

[overview] =>

In the first part of this training, we cover the fundamentals of MATLAB and its function as both a language and a platform.  Included in this discussion is an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning and predictive analytics. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and using other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating their data processing and report generation.

Throughout the course, participants will put into practice the ideas learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough grasp of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

Note

[category_overview] => [outline] =>

Introduction
MATLAB for data science and reporting

 

Part 01: MATLAB Fundamentals

Overview

Working with the MATLAB user interface

Overview of MATLAB syntax

Entering commands

Creating variables

Analyzing vectors and matrices

Visualizing vector and matrix data

Working with data files

Working with data types

Automating commands with scripts

Writing programs with branching and loops

Writing functions

Applying object-oriented programming principles to your programs

 

Part 02: MATLAB for Data Science

Overview

Accessing data

Exploring data

Creating customized algorithms

Creating visualizations

Creating models

Publishing customized reports

Sharing analysis tools

Using the Statistics and Machine Learning Toolbox

Using the Neural Network Toolbox

 

Part 03: Report Generation

Overview

Creating reports interactively vs programmatically

Creating reports interactively using Report Explorer

Creating reports programmatically in MATLAB


Summary and Closing Remarks

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals, Data Science & Report Generation [source_language] => en [cert_code] => [weight] => -996 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdsandreporting ) [matlabdynamicanalysis] => stdClass Object ( [course_code] => matlabdynamicanalysis [hr_nid] => 440063 [title] => Dynamic Analysis Using Matlab [requirements] =>

Audience

[overview] =>

Dynamic analysis is the process of testing and evaluating a material or program while running a software.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at beginner-level developers or engineers who wish to learn how to use numerical simulation for dynamic problems using Matlab.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Overview of Matlab

Calculation of Natural Values Using Matlab

Dynamic Analysis of Material

Motion Analysis of Material

Vibration Analysis

Governing Equation of Motion

Closed-Form Analysis

Matlab Programming on Analytical Solutions

Numerical Analysis

Matlab Programming on Numerical Solutions

Summary and Next Steps

[language] => en [duration] => 21 [status] => published [changed] => 1700037948 [source_title] => Dynamic Analysis Using Matlab [source_language] => en [cert_code] => [weight] => -1001 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabdynamicanalysis ) [matlabfincance] => stdClass Object ( [course_code] => matlabfincance [hr_nid] => 205333 [title] => Matlab for Finance [requirements] =>

Course options

[overview] =>

MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics.

This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice.

By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems.

Audience

Format of the course

[category_overview] => [outline] =>

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1715349844 [source_title] => Matlab for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfincance ) [matlabfundamentalsfinance] => stdClass Object ( [course_code] => matlabfundamentalsfinance [hr_nid] => 205417 [title] => MATLAB Fundamentals + MATLAB for Finance [requirements] => [overview] =>

This course provides a comprehensive introduction to the MATLAB technical computing environment + an introduction to using MATLAB for financial applications. The course is intended for beginning users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Topics include:

[category_overview] => [outline] =>

Part 1

A Brief Introduction to MATLAB

Objectives: Offer an overview of what MATLAB is, what it consists of, and what it can do for you

Working with the MATLAB User Interface

Objective: Get an introduction to the main features of the MATLAB integrated design environment and its user interfaces. Get an overview of course themes.

Variables and Expressions

Objective: Enter MATLAB commands, with an emphasis on creating and accessing data in variables.

Analysis and Visualization with Vectors

Objective: Perform mathematical and statistical calculations with vectors, and create basic visualizations. See how MATLAB syntax enables calculations on whole data sets with a single command.

Analysis and Visualization with Matrices

Objective: Use matrices as mathematical objects or as collections of (vector) data. Understand the appropriate use of MATLAB syntax to distinguish between these applications.

Part 2

Automating Commands with Scripts

Objective: Collect MATLAB commands into scripts for ease of reproduction and experimentation. As the complexity of your tasks increases, entering long sequences of commands in the Command Window becomes impractical.

Working with Data Files

Objective: Bring data into MATLAB from formatted files. Because imported data can be of a wide variety of types and formats, emphasis is given to working with cell arrays and date formats.

Multiple Vector Plots

Objective: Make more complex vector plots, such as multiple plots, and use color and string manipulation techniques to produce eye-catching visual representations of data.

Logic and Flow Control

Objective: Use logical operations, variables, and indexing techniques to create flexible code that can make decisions and adapt to different situations. Explore other programming constructs for repeating sections of code, and constructs that allow interaction with the user.

Matrix and Image Visualization

Objective: Visualize images and matrix data in two or three dimensions. Explore the difference in displaying images and visualizing matrix data using images.

Part 3

Data Analysis

Objective: Perform typical data analysis tasks in MATLAB, including developing and fitting theoretical models to real-life data. This leads naturally to one of the most powerful features of MATLAB: solving linear systems of equations with a single command.

Writing Functions

Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables.

Data Types

Objective: Explore data types, focusing on the syntax for creating variables and accessing array elements, and discuss methods for converting among data types. Data types differ in the kind of data they may contain and the way the data is organized.

File I/O

Objective: Explore the low-level data import and export functions in MATLAB that allow precise control over text and binary file I/O. These functions include textscan, which provides precise control of reading text files.

Note that the actual delivered might be subject to minor discrepancies from the outline above without prior notification.

Part 4

Overview of the MATLAB Financial Toolbox

Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data.

Asset Allocation and Portfolio Optimization

Objective: perform capital allocation, asset allocation, and risk assessment.

Risk Analysis and Investment Performance

Objective: Define and solve portfolio optimization problems.

Fixed-Income Analysis and Option Pricing

Objective: Perform fixed-income analysis and option pricing.

Part 5

Financial Time Series Analysis

Objective: analyze time series data in financial markets.

Regression and Estimation with Missing Data

Objective: Perform multivariate normal regression with or without missing data.

Technical Indicators and Financial Charts

Objective: Practice using performance metrics and specialized plots.

Monte Carlo Simulation of SDE Models

Objective: Create simulations and apply SDE models

Conclusion

Objectives: Summarise what we have learned

Note: the actual content delivered might differ from the outline as a result of customer requirements and the time spent on each topic.

[language] => en [duration] => 35 [status] => published [changed] => 1715349844 [source_title] => MATLAB Fundamentals + MATLAB for Finance [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabfundamentalsfinance ) [matlabml1] => stdClass Object ( [course_code] => matlabml1 [hr_nid] => 97759 [title] => MATLAB与机器学习入门 [requirements] =>

Good math skills
Linear Algebra

[overview] =>

MATLAB is a numerical computing environment and programming language developed by MathWorks.

[category_overview] => [outline] =>
  1. MATLAB Basics
  2. MATLAB More Advanced Features
  3. BP Neural Network
  4. RBF, GRNN and PNN Neural Networks
  5. SOM Neural Networks
  6. Support Vector Machine, SVM
  7. Extreme Learning Machine, ELM
  8. Decision Trees and Random Forests
  9. Genetic Algorithm, GA
  10. Particle Swarm Optimization, PSO
  11. Ant Colony Algorithm, ACA
  12. Simulated Annealing, SA
  13. Dimenationality Reduction and Feature Selection
[language] => en [duration] => 21 [status] => published [changed] => 1715592376 [source_title] => MATLAB与机器学习入门 [source_language] => zh-hans [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabml1 ) [matlabpredanalytics] => stdClass Object ( [course_code] => matlabpredanalytics [hr_nid] => 212828 [title] => Matlab for Predictive Analytics [requirements] => [overview] =>

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

Audience

Format of the course

[category_overview] => [outline] =>

Introduction

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

[language] => en [duration] => 21 [status] => published [changed] => 1715349914 [source_title] => Matlab for Predictive Analytics [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabpredanalytics ) [matlabprog] => stdClass Object ( [course_code] => matlabprog [hr_nid] => 2842 [title] => MATLAB Programming [requirements] => [overview] =>

This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course.

[category_overview] => [outline] => [language] => en [duration] => 14 [status] => published [changed] => 1700037086 [source_title] => MATLAB Programming [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => hitrapl [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => matlabprog ) [octnp] => stdClass Object ( [course_code] => octnp [hr_nid] => 202281 [title] => Octave not only for programmers [requirements] => [overview] =>

Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.

[category_overview] => [outline] =>

Introduction

Simple calculations

The Octave environment

Arrays and vectors

Plotting graphs

Octave programming I: Script files

Control statements

Octave programming II: Functions

Matrices and vectors

Linear and Nonlinear Equations

More graphs

 Eigenvectors and the Singular Value Decomposition

 Complex numbers

 Statistics and data processing

 GUI Development

[language] => en [duration] => 21 [status] => published [changed] => 1700037275 [source_title] => Octave not only for programmers [source_language] => en [cert_code] => [weight] => 0 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => octnp ) [pythonformatlab] => stdClass Object ( [course_code] => pythonformatlab [hr_nid] => 306994 [title] => Python for Matlab Users [requirements] =>

Audience

[overview] =>

The Python programming language is becoming more and more popular among Matlab users due to its power and versatility as a data analysis tool as well as a general purpose language.

This instructor-led, live training (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

Format of the Course

Course Customization Options

[category_overview] =>

This instructor-led, live training in <loc> (online or onsite) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization.

By the end of this training, participants will be able to:

[outline] =>

Introduction

Setting up a Python Development Environment for Data Science

The Power of Matlab for Numerical Problem Solving

Python Libraries and Packages for Numerical Problem Solving and Data Analysis

Hands-on Practice with Python Syntax

Importing Data into Python

Matrix Manipulation

Math Operations

Visualizing Data

Converting an Existing Matlab Application to Python

Common Pitfalls when Transitioning to Python

Calling Matlab from within Python and Vice Versa

Python Wrappers for Providing a Matlab-like Interface

Summary and Conclusion

[language] => en [duration] => 14 [status] => published [changed] => 1700037484 [source_title] => Python for Matlab Users [source_language] => en [cert_code] => [weight] => -977 [excluded_sites] => [use_mt] => stdClass Object ( [field_overview] => [field_course_outline] => [field_prerequisits] => [field_overview_in_category] => ) [cc] => pythonformatlab ) [simulinkadv] => stdClass Object ( [course_code] => simulinkadv [hr_nid] => 198633 [title] => Simulink® for Automotive System Design Advanced Level [requirements] =>

Participants should have basic knowledge about Simulink

[overview] =>

Simulink is a graphical programming environment for modeling, simulating and analyzing multidomain dynamic systems.

[category_overview] => [outline] =>
  1. Conditionally executed subsystems
  2. Enabled subsystems
  3. Triggered subsystems
  4. Input validation model

Create a simple Simulink model, simulate it, and analyze the results.

  1. Define the potentiometer system
  2. Explore the Simulink environment interface
  3. Create a Simulink model of the potentiometer system
  4. Simulate the model and analyze results
  1. Comparisons and decision statements
  2. Zero crossings
  3. MATLAB Function block

Modeling Discrete Systems Objective:

Model and simulate discrete systems in Simulink.

  1. Define discrete states
  2. Create a model of a PI controller
  3. Model discrete transfer functions and state space systems
  4. Model multirate discrete systems

Modeling Continuous Systems:

Model and simulate continuous systems in Simulink.

  1. Create a model of a throttle system
  2. Define continuous states
  3. Run simulations and analyze results
  4. Model impact dynamics

Solver Selection: Select a solver that is appropriate for a given Simulink model.

  1. Solver behavior
  2. System dynamics
  3. Discontinuities
  4. Algebraic loops
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