[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-968263","data":"\u003Cdiv class=\u0022container content-rte\u0022\u003E\n  \u003Cdiv class=\u0022row\u0022\u003E\n    \u003Cdiv class=\u0022box col-md-6 bg-primary text-white\u0022\u003E\n      \u003Ch2 class=\u0022h1 text-left\u0022\u003E  Analyse des donn\u00e9es\n\u003C\/h2\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n          4\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n                  \u003Cdiv class=\u0022field-spacing\u0022\u003E\n        \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemestre calendaire :\n          Semester 1\n        \u003C\/div\u003E\n      \u003C\/div\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ED\u00e9tail du volume horaire\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ECours magistraux :\u003C\/span\u003E\n                                      \u003Cspan\u003E21\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                              \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                          \u003Cspan class=\u0022field__label h4 mt-5 \u0022\u003ETravaux dirig\u00e9s :\u003C\/span\u003E\n                                      \u003Cspan\u003E21\u003C\/span\u003E\n                      \u003C\/div\u003E\n                                                            \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ELangue d\u0026#039;enseignement\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EAnglais\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EEnseignement \u00e0 distance\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003Enon\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n    \u003Cdiv class=\u0022box col-md-6\u0022\u003E\n            \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EPr\u00e9requis\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cul\u003E\n\u003Cli\u003Ealg\u00e8bre lin\u00e9aire\u003C\/li\u003E\n\u003Cli\u003Estatistique inf\u00e9rentielle de base\u003C\/li\u003E\n\u003Cli\u003Eoptimisation num\u00e9rique\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EEn Fran\u00e7ais :\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003ELoi normale multivari\u00e9e.\u003C\/li\u003E\n\u003Cli\u003EClassification automatique: K-means, Classification hi\u00e9rarchique.\u003C\/li\u003E\n\u003Cli\u003EMod\u00e8les de m\u00e9lange gaussien.\u003C\/li\u003E\n\u003Cli\u003EAnalyses factorielles: Analyse en composantes principales, Analyse factorielle d\u2019un tableau de distances.\u003C\/li\u003E\n\u003Cli\u003EM\u00e9thodes \u00e0 noyaux: Analyse en composantes principales \u00e0 noyaux, Spectral Clustering.\u003C\/li\u003E\n\u003Cli\u003EMod\u00e8les graphiques.\u003C\/li\u003E\n\u003C\/ul\u003E\n\u003Cp\u003EIn English:\u003C\/p\u003E\n\u003Cul\u003E\n\u003Cli\u003EMultivariate Gaussian Distribution.\u003C\/li\u003E\n\u003Cli\u003EClustering: K-means, Hierarchical clustering.\u003C\/li\u003E\n\u003Cli\u003EGaussian Mixture Models.\u003C\/li\u003E\n\u003Cli\u003EFactorial analyses: Principal component analysis, Principal coordinate analysis.\u003C\/li\u003E\n\u003Cli\u003EKernel methods: Kernel PCA, Spectral Clustering.\u003C\/li\u003E\n\u003Cli\u003EGraphical Models.\u003C\/li\u003E\n\u003C\/ul\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EEn Fran\u00e7ais :\u003Cbr\u003E\nIntroduire \u00e0 l\u2019analyse des donn\u00e9es : th\u00e9orie et pratique d\u2019une famille de m\u00e9thodes (parfois statistiques) permettant d\u2019explorer de donn\u00e9es repr\u00e9sent\u00e9es principalement sous forme de tableau o\u00f9 chaque ligne d\u00e9crit un objet (individu) et chaque colonne une variable.\u003C\/p\u003E\n\u003Cp\u003EIn English:\u003Cbr\u003E\nIntroduction to data analysis: theory and practice of a family of (sometimes statistical) methods for exploring data represented mainly by a datatable where each line describes an object and each column a variable.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EOrganisation g\u00e9n\u00e9rale et modalit\u00e9s p\u00e9dagogiques\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003E21 heures de cours (12 s\u00e9ances de 1h45) 21 heures de TD (12 s\u00e9ance de 1h45). Exercices th\u00e9oriques et pratiques sous R.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n                        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EBibliographie\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EBishop, Christopher M. Pattern recognition and machine learning. Springer Science+ Business Media, 2006.\u003C\/p\u003E\n\u003Cp\u003EFriedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001.\u003C\/p\u003E\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n        \u003Cdiv class=\u0022field-spacing\u0022\u003E\n      \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ENature de l\u0026#039;\u00e9valuation\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003EEvaluation Continue non Int\u00e9grale\u003C\/span\u003E\n                      \u003C\/div\u003E\n                  \u003C\/div\u003E\n      \u003C\/div\u003E\n  \u003C\/div\u003E\n\u003C\/div\u003E\n","settings":null}]