[{"command":"insert","method":null,"selector":".course-modal-ajax-placeholder-955867","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 Fouille de donn\u00e9es pour le vivant\n\u003C\/h2\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003EECTS :\n 2\n \u003C\/div\u003E\n \u003C\/div\u003E\n \u003Cdiv class=\u0022field-spacing\u0022\u003E\n \u003Cdiv class=\u0022field__label h3 dash\u0022\u003ESemester:\n Semestre 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\u003ELecture:\u003C\/span\u003E\n \u003Cspan\u003E10\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\u003EPractical study :\u003C\/span\u003E\n \u003Cspan\u003E10\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\u003EFran\u00e7ais\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\u003EDes bases en algorithmique et programmation (id\u00e9alement en Python) sont n\u00e9cessaires.\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\u003ESont abord\u00e9es dans ce module des \u00e9l\u00e9ments th\u00e9oriques permettant de comprendre les questions fondamentales de l\u2019apprentissage non supervis\u00e9. Y seront pr\u00e9sent\u00e9s et d\u00e9taill\u00e9s quelques mod\u00e8les, ainsi que des \u00e9l\u00e9ments de m\u00e9thodologie et de mise en pratique.\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\u003ELa fouille de donn\u00e9es (data mining) est un domaine de l\u0027intelligence artificielle qui s\u0027int\u00e9resse \u00e0 l\u0027\u00e9mergence de connaissances nouvelles \u00e0 partir de donn\u00e9es. On parle \u00e9galement d\u0027apprentissage automatique non supervis\u00e9 (unsupervised learning). L\u0027objectif de ce module est d\u0027\u00e9tudier les probl\u00e9matiques de l\u0027apprentissage automatique non supervis\u00e9 et les diff\u00e9rentes cat\u00e9gories de m\u00e9thodes qui ont \u00e9t\u00e9 propos\u00e9es pour y r\u00e9pondre avec des applications dans le domaine des sciences du vivant et l\u0027environnement. On se place dans un cadre \u0022non supervis\u00e9\u0022 lorsqu\u0027on n\u0027a pas d\u0027exemples pour lesquelles on conna\u00eet ce que l\u0027on cherche \u00e0 pr\u00e9dire. Par exemple, on dispose d\u0027une collection d\u0027images. Certaines repr\u00e9sentent des plantes et d\u0027autres des animaux mais on ne sait pas \u00e0 quelle \u0022cat\u00e9gorie\u0022 appartient chacune de nos images. Parfois, il est possible et facile d\u0027obtenir ce type d\u0027informations et un apprentissage automatique dit \u0022supervis\u00e9\u0022 (supervised learning) sera envisageable. Dans le cas contraire, comme souvent en sciences du vivant et de l\u0027environnement o\u00f9 les \u0022\u00e9tiquettes\u0022 peuvent \u00eatre ch\u00e8res, difficiles voire impossible \u00e0 obtenir, il faudra abord\u00e9 le probl\u00e8me de mani\u00e8re \u0022non supervis\u00e9e\u0022 (unsupervised learning). C\u0027est ce que nous verrons lors de ce module.\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\u003ECe module se d\u00e9coupe naturellement en deux parties. L\u0027une ax\u00e9e sur la probl\u00e9matique de la classification (clustering) et l\u0027autre sur les approches par motifs (patterns mining).\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\u003E\u0022A survey of sequential pattern mining\u0022, P Fournier-Viger, JCW Lin, RU Kiran, YS Koh, R Thomas, Data Science and Pattern Recognition 1 (1), 54-77\u003C\/p\u003E\n\u003Cp\u003E\u0022A survey of utility-oriented pattern mining\u0022, W Gan, JCW Lin, P Fournier-Viger, HC Chao, VS Tseng, PS Yu,\u0026nbsp;\u003Cbr\u003E\nIEEE Transactions on Knowledge and Data Engineering 33 (4), 1306-1327\u003C\/p\u003E\n\u003Cp\u003E\u0022A survey of itemset mining\u0022, P Fournier\u2010Viger, JCW Lin, B Vo, TT Chi, J Zhang, HB Le, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7\u0026nbsp;\u003C\/p\u003E\n\u003Cp\u003ES. P. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol. 28, n\u00b02, pp. 129\u2013137, mars 1982 DOI 10.1109\/TIT.1982.1056489\u003C\/p\u003E\n\u003Cp\u003EM. Ester, H.-P. Kriegel, J. Sander, and X. Xu, \u201cA density-based algorithm for discovering clusters in large spatial databases with noise,\u201d in Proceedings of the 2nd International Conference on Knowledge Discovery and Data mining, 1996, pp. 226\u2013231.\u003C\/p\u003E\n\u003Cp\u003EMihael Ankerst, Markus M. Breunig,\u0026nbsp;Hans-Peter Kriegel, J\u00f6rg Sander (1999). \u00ab\u0026nbsp;OPTICS: Ordering Points To Identify the Clustering Structure\u00bb, ACM SIGMOD international conference on Management of data : 49\u201360 p.,\u0026nbsp;ACM Press.\u003C\/p\u003E\n\u003Cp\u003EAhmed, M.; Seraj, R.; Islam, S.M.S. The k-means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics 2020, 9, 1295.\u0026nbsp;https:\/\/doi.org\/10.3390\/electronics9081295\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}]