Scolarité M2 (sauf MIC, MIDS)
- Mme Chatoux
- bureau 5055
- 01 57 27 93 06
- Mme Prudlo
- bureau 5055
- 01 57 27 93 06
Introduction au Machine Learning
6 ECTS, semestre 1, 10 semaines
| Prérequis | Optimisation M1. Statistique M1 |
| Validation | CC+examen |
| Enseignant | Stéphane Gaïffas |
| Horaires hebdomadaires | 3 h CM , 2 h TD |
| Années | M2 Data Science (ouverture 2020) |
Syllabus
Présentation des méthodes d'apprentissage supervisé et non-supervisé: des modèles génératifs aux réseaux de neurones en passant par les techniques arborescentes.
Sommaire
- Introduction to supervised learning (3 weeks). Binary classification, standard metrics and recipes (overfitting, cross-validation) and regression, LDA / QDA, Logistic regression, Generalized Linear Models, Regularization (Ridge, Lasso), Support Vector Machine, the Hinge loss. Kernel methods. Decision trees, CART, Boosting.
- Optimization for Machine Learning (2 weeks). Proximal gradient descent, Coordinate descent / coordinate gradient descent, Quasi-newton methods, Stochastic gradient descent and beyond
- Neural Networks (2 weeks). Introduction to neural networks. The perceptron, multilayer neural networks, deep learning. Adaptive-rate stochastic gradient descent, back-propagation. Convolutional neural networks
- Unsupervised learning (2 sessions). Gaussian mixtures and EM, Matrix Factorization, Non-negative Matrix Factorization, Factorization machines, Embeddings methods
Bibliographie
- Murphy, K.M. (2012). Machine Learning. MIT Press.
- Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2012). Foundations of Machine Learning. MIT Press.
- Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
- McKinney, W. (2012). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly.
- Bühlmann, P., and van de Geer, S. (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer-Verlag.