Module catalogue 2018/2019
| 2IAG1 | Data analysis and machine learning | Computer Science - Apprenticeship | S8 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Lessons : 11 h | TD : 7 h | TP : 11 h | Project : 0 h | Total : 29 h | |||||
| Co-ordinator : Christophe ROSENBERGER | |||||||||
| Prerequisite | |
|---|---|
| Probability and statistics | |
| Course Objectives | |
| Students learn how to implement algorithms to extract knowledge from data, realize machine learning (statistical learning) or solve complex problems. | |
| Syllabus | |
| - Statistical analysis - Bayesian network - Case based reasonning - Neural networks - statistical learning - Evolutionnary algorithms |
|
| Practical work (TD or TP) | |
| Practical work with Matlab Data analysis of the Titanic file Problem resolution with genetic algorithms Learning with a perceptron pour heart diseases |
|
| Acquired skills | |
| Knowledge on Machine learning Knowledge on evolutionnary algorithms |
|
| Bibliography | |
| - 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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