Module : Machine Learning
| Semestre 6 SC | VHS C/TD/TP |
VHH Total C/TD/TP |
V.H. Hebdomadaire | Coef | Crédits | ||
|---|---|---|---|---|---|---|---|
| C | TD | TP | |||||
| UE Fondamentales 6.1 | 112.5 | 7.5 | 3 | 1.5 | 3 | 4 | 6 |
Course Description:
This course provides a broad introduction to machine learning and statistical pattern recognition. Students will be introduced to many machine learning algorithms and methods such as regressions, decision trees and support vector machines. They will understand the mathematical foundations of the different machine learning algorithms as well as acquire practical experience in using them for the different types of data.
Prerequisite : Linear Algebra, Probability and Inference, Programming
Evaluation Method : Coursework (40 %) + Final Exam (60%)
Course Content:
- Introduction to Machine Learning
- Linear models for regression and classification
- Nonlinear regression
- Decision Trees
- Instance-Based Learning
- Support Vector Machine
- Ensemble Methods
- Introduction to Neural Networks
- Performance Analysis
- Case Studies
References
- Andrew R. Webb, Keith D. Copsey, Statistical Pattern Recognition, 3rd Edition, 2011
- Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2000.
- C. Bishop : Pattern Recognition and Machine Learning. Springer: New York,2006.
- A. Cornuéjols and L. Miclet : Apprentissage artificiel – Concepts et algorithmes. Eyrolles, 2010.
- T. Hastie, R. Tibshirani and Friedman, The Elements of Statistical Learning, Springer. [ebook]
- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning , Springer, 2013
- K. P. Murphy : Machine Learning: a Probabilistic Perspective. MIT Press, 2012.
- B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996. https://www.cs.purdue.edu/homes/clifton/cs490d/