Module catalogue 2023/2024
| 434-1 | Machine Learning | Computer Science | S9 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Lessons : 20 h | TD : 2 h | TP : 8 h | Project : 0 h | Total : 30 h | |||||
| Co-ordinator : Regis Clouard | |||||||||
| Prerequisite | |
|---|---|
| Basics of algebra, scientific computation and optimization | |
| Course Objectives | |
| This module presents the methods and algorithms for machine learning. | |
| Syllabus | |
| The module focuses on statistical learning and data mining methods. After an introduction to the different types of statistical learning problems (supervised, unsupervised, etc.) by illustrating them in the context of data mining, the principles on which statistical learning methods are based will be developed (notions of structural / empirical risks, bias / variance tradeoffs, model validation, etc.). The main classes of existing statistical learning methods will then be detailed: nearest neighbors, neural networks, kernel-based methods, dimension reduction, etc. The data mining part focuses on sequential patterns from a methodological point of view as well as from a usage point of view through their utility for textual data mining. | |
| Practical work (TD or TP) | |
| The course will be illustrated by applications on multimedia data (text, image, etc.) | |
| Acquired skills | |
| Knowledge of statistical learning and data mining | |
| Bibliography | |
| Alpaydin, E. (2010). Introduction to Machine Learning (2nd ed.). The MIT Press. Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning (2nd ed.). Springer-Verlag New York, Inc., Secaucus, NJ, USA. Bishop, C.M. (2006). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA. Mitchell, T. (1997). Machine Learning (1 ed.). McGraw-Hill, Inc., New York, NY, USA. |
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