{"id":16439,"date":"2025-03-11T14:43:13","date_gmt":"2025-03-11T13:43:13","guid":{"rendered":"https:\/\/cyberschool.univ-rennes.fr\/?page_id=16439"},"modified":"2026-03-12T14:30:55","modified_gmt":"2026-03-12T13:30:55","slug":"apprentissage-statistique","status":"publish","type":"page","link":"https:\/\/cyberschool.univ-rennes.fr\/en\/education\/masters-and-postgraduate-programmes\/masters-in-mathematics-and-applications-it-mathematics-and-cryptography\/study-programme-of-the-masters-mathematics-and-applications-it-mathematics-and-cryptography\/apprentissage-statistique\/","title":{"rendered":"Apprentissage statistique"},"content":{"rendered":"\n
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<\/span><\/span>Course description<\/h2>\n

L\u2019apprentissage (machine learning) a pour but de trouver des algorithmes permettant de pr\u00e9dire certaines variables (cach\u00e9es) \u00e0 l\u2019aide d\u2019autres (observ\u00e9es). Ces algorithmes sont mis au point \u00e0 l\u2019aide d\u2019un ensemble d\u2019apprentissage o\u00f9 tout est observ\u00e9. C\u2019est donc le lien entre variables qui doit \u00eatre d\u00e9crypt\u00e9. Le cours est constitu\u00e9 de 9 chapitres qui introduisent les notions de base utiles \u00e0 la compr\u00e9hension de ces algorithmes :<\/p>\n

    \n
  1. Analyse factorielle<\/li>\n
  2. Classification non supervis\u00e9e (clustering)<\/li>\n
  3. Analyse discriminante<\/li>\n
  4. R\u00e9gression lin\u00e9aire multiple<\/li>\n
  5. Ridge, lasso, et ACP (analyse en composantes principales)<\/li>\n
  6. M\u00e9thodes des k plus proches voisins et estimateur \u00e0 noyau<\/li>\n
  7. Arbres de d\u00e9cision ; validation\/comparaison pour les algorithmes de classifications<\/li>\n
  8. Agr\u00e9gation de mod\u00e8les : bagging, for\u00eats al\u00e9atoires, boosting et gradient boosting<\/li>\n
  9. R\u00e9seaux de neurones et deep learning<\/li>\n<\/ol>\n\n <\/div>\n <\/div>\n<\/section>\n\n\n\n
    \n
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    \n

    <\/span><\/span>Skills to acquire<\/h2>\n

    \u00c0 l\u2019issue de ce cours, les \u00e9tudiants seront capables de choisir une m\u00e9thode d\u2019apprentissage, de la mettre en \u0153uvre, et de la valider.<\/p>\n\n <\/div>\n <\/div>\n<\/section>\n\n\n\n

    \n
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    <\/span><\/span>Biography<\/h2>\n

    Cl\u00e9ment Levrard<\/strong> est\u00a0 professeur \u00e0 l\u2019Universit\u00e9 de Rennes.<\/p>\n\n <\/div>\n <\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":8,"featured_media":0,"parent":11605,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"acf":[],"yoast_head":"\nApprentissage statistique - CyberSchool<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cyberschool.univ-rennes.fr\/en\/education\/masters-and-postgraduate-programmes\/masters-in-mathematics-and-applications-it-mathematics-and-cryptography\/study-programme-of-the-masters-mathematics-and-applications-it-mathematics-and-cryptography\/apprentissage-statistique\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apprentissage statistique - 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