{"id":950,"date":"2022-01-21T20:45:56","date_gmt":"2022-01-21T19:45:56","guid":{"rendered":"https:\/\/mastermas.univ-lyon1.fr\/?page_id=950"},"modified":"2022-01-21T20:45:56","modified_gmt":"2022-01-21T19:45:56","slug":"machine-learning","status":"publish","type":"page","link":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/machine-learning\/","title":{"rendered":"MACHINE LEARNING"},"content":{"rendered":"\n<p>&nbsp;<strong>Plan <\/strong><strong>du module :<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Tour d\u2019horizon des probl\u00e8mes &amp; types d\u2019apprentissage (supervis\u00e9\/non supervis\u00e9, classification\/r\u00e9gression, single\/multi output, structur\u00e9\/non structur\u00e9, statistiques ou non, etc.).<\/li><li>Principaux mod\u00e8les et algorithmes d\u2019apprentissage supervis\u00e9 (mod\u00e8les lin\u00e9aires, r\u00e9seaux de neuronnes, arbres de d\u00e9cision, <em>Bagging, Random Forest, Boosting<\/em>) et d\u2019apprentissage non&nbsp; supervis\u00e9 (K-means, clustering hi\u00e9rarchiques, etc.)<\/li><li>Les concepts importants pr\u00e9paration de donn\u00e9es, fonctions co\u00fbt, crit\u00e8res de performance, <em>overfitting<\/em>, dilemme biais-variance, validation crois\u00e9e, donn\u00e9es d\u00e9s\u00e9quilibr\u00e9es, donn\u00e9es manquantes, cr\u00e9ation des variables, etc..<\/li><li>S\u00e9lection de variables et de mod\u00e8les<\/li><li>Apprentissage multi-label et multi-r\u00e9gression<\/li><li>D\u00e9tection d\u2019anomalies<\/li><li>Text Mining&nbsp;: Pr\u00e9paration de donn\u00e9es, TF-IDF, LSI, Word Embedding, etc.<\/li><li>Mise en pratique sur des jeux de donn\u00e9es avec <em>scikit-learn<\/em><a href=\"#_ftn1\"><sup>[1]<\/sup><\/a> sous <em>Python<\/em> sur des cas d&rsquo;\u00e9tudes r\u00e9els.<\/li><\/ul>\n\n\n\n<hr class=\"wp-block-separator\"\/>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp;Plan du module : Tour d\u2019horizon des probl\u00e8mes &amp; types d\u2019apprentissage (supervis\u00e9\/non supervis\u00e9, classification\/r\u00e9gression, single\/multi output, structur\u00e9\/non structur\u00e9, statistiques ou non, etc.). Principaux mod\u00e8les et algorithmes d\u2019apprentissage supervis\u00e9 (mod\u00e8les lin\u00e9aires, r\u00e9seaux de neuronnes, arbres de d\u00e9cision, Bagging, Random Forest, Boosting) et d\u2019apprentissage non&nbsp; supervis\u00e9 (K-means, clustering hi\u00e9rarchiques, etc.) Les concepts importants pr\u00e9paration de donn\u00e9es, fonctions <a class=\"more-link\" href=\"https:\/\/mastermas.univ-lyon1.fr\/index.php\/machine-learning\/\">Lire plus &#8230;<\/a><\/p>\n","protected":false},"author":6,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-950","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/pages\/950","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/comments?post=950"}],"version-history":[{"count":1,"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/pages\/950\/revisions"}],"predecessor-version":[{"id":951,"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/pages\/950\/revisions\/951"}],"wp:attachment":[{"href":"https:\/\/mastermas.univ-lyon1.fr\/index.php\/wp-json\/wp\/v2\/media?parent=950"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}