Digital educational booklet 2016/2017
| 2IAI1 | Data mining and machine learning | Computer Science - Apprentissage | S8 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Cours : 11 h | TD : 7 h | TP : 11 h | Projet : 0 h | Total : 29 h | |||||
| Responsable : Christophe ROSENBERGER | |||||||||
| Pré-requis | |
|---|---|
| probability and statistics | |
| Objectifs de l'enseignement | |
| Students learn how to implement algorithms to extract knowledge from data, realize machine learning (statistical learning) or solve complex problems. | |
| Programme détaillé | |
| - Statistical analysis - Bayesian network - Case based reasonning - Neural networks - statistical learning - Evolutionnary algorithms |
|
| Applications (TD ou TP) | |
| Practical work with Matlab Data analysis of the Titanic file Problem resolution with genetic algorithms Learning with a perceptron pour heart diseases |
|
| Compétences acquises | |
| Knowledge on Machine learning Knowledge on evolutionnary algorithms |
|
| Bibliographie | |
| - Gérard Dreyfus , Gérard Dreyfus , Jean-Marc Martinez , Manuel Samuelides "Apprentissage statistique", Edition Eyrolles - Cornuéjols, A and Miclet L.: Apprentissage Artificiel. Concepts et algorithmes (2nd Ed.with revisions and additions - 2006 Eyrolles, 650 p - Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer (2006). |
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