[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-968268","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  Mod\u00e9lisation statistique\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 2\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\u003Cp\u003EEn Fran\u00e7ais\u0026nbsp;:\nFondamentaux de probabilit\u00e9s, de statistiques et d\u2019alg\u00e8bre lin\u00e9aire. Notions de programmation.\u003C\/p\u003E\n\u003Cp\u003EIn English:\nBasics of probabilities, statistics and linear algebra. Skills in programming.\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\u003EProgramme \/ plan \/ contenus\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EEn Fran\u00e7ais\u0026nbsp;:\nMod\u00e9lisation de series temporelles:\nLissage, estimation de tendance et de saisonnalit\u00e9, processus stationnaires, mod\u00e8le ARMA, approche de Box-Jenkins et pr\u00e9diction.\nApprentissage statististique supervis\u00e9\nM\u00e9thodologie de l\u2019apprentissage statistique : minimisation du risque empirique et du risque structurel, \u00e9valuation de l\u2019erreur de g\u00e9n\u00e9ralisation.\nM\u00e9thodes : SVM, boosting et arbres de d\u00e9cisions, r\u00e9seaux bay\u00e9siens.\u003C\/p\u003E\n\u003Cp\u003EIn English:\nTime Series Modeling:\nSmoothing, trend and seasonality estimation, stationary processes, ARMA, Box-Jenkins estimation and prediction\nSupervised Machine Learning\nConcepts and methodology: Empirical Risk Minimization, Structural risk, Generalization error\nMethods : SVM, boosting and decision trees, Bayesian netoworks.\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\u003EObjectifs d\u0026#039;apprentissage\u003C\/div\u003E\n                        \u003Cdiv class=\u0022mt-5 text-justify\u0022\u003E\n                                      \u003Cspan\u003E\u003Cp\u003EEn Fran\u00e7ais\u0026nbsp;:\nCe cours pr\u00e9sente un ensemble de m\u00e9thodes permettant de d\u00e9couvrir les relations existantes entre la r\u00e9ponse et les observations sur plusieurs variables (qualitatives, quantitatives ou temporelles) d\u2019un ph\u00e9nom\u00e8ne al\u00e9atoire. Le point de vue de la mod\u00e9lisation sera adopt\u00e9 pour \u00e9tudier les s\u00e9ries temporelles. En parall\u00e8le, on pr\u00e9sentera le point de vue de l\u2019apprentissage machine dans le cadre supervis\u00e9.\u003C\/p\u003E\n\u003Cp\u003EIn English:\nThis course presents a set of methods allowing to discover the relations between labels and observations (qualitative, quantitative or temporal) of a random phenomenon. On one hand, time series will be studied according to modeling aspects. On the other hand, we will present machine learning in the supervised framework.\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\u003EEn Fran\u00e7ais :\nMod\u00e9lisation de s\u00e9ries temporelles: 6 cours (1h45) et 6 TP (1h45) Apprentissage statistique supervis\u00e9: 6 cours (1h45) et 6 TP (1h45).\u003C\/p\u003E\n\u003Cp\u003EIn English:\nTime Series Modeling: 6 sessions (1h45) of lecutre and 6 sessions of tutorials\nSupervized Machine Learning: 6 sessions (1h45) of lecutre and 6 sessions of tutorials.\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\u003EThe elements of statistical learning. T. Hastie, R. Tibshirani, J. Friedman https:\/\/web.stanford.edu\/~hastie\/ElemStatLearn\/\u003C\/p\u003E\n\u003Cp\u003EPattern Recognition And Machine Learning. C. Bishop.\u003C\/p\u003E\n\u003Cp\u003EApprentissage artificiel. A. Cornu\u00e9jols, L. Miclet, V. Barra.\u003C\/p\u003E\n\u003Cp\u003EIntroduction to Time Series and Forecasting. Peter J. Brockwell, Richard A. Davis.\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}]