Machine learning

Crédit : 5 ECTS
Langue du cours : anglais

Volume horaire

• CM : 39 h
• Volume horaire global (hors stage) : 39 h

Compétences à acquérir

Introduction to statistical learning, particularly in a high-dimensional context, including baseline algorithms (k-NN,...) and modern approaches in deep learning (neural networks).

Description du contenu de l'enseignement

1. Examples and machine learning framework: applications, supervised and non-supervised learning
2. Useful theoretical objects: predictors, loss functions, bias, variance
3. K-nearest neighbors (k-NN); Higher dimensions and Curse of dimensionality
4. Regularization in high dimensions: ridge and lasso (for linear and logistic models)
5. Stochastic Optimization Algorithms used in machine learning: Stochastic Gradient Descent, Momentum, Adam, RMSProp
6. Naive Bayesian classification
7. Deep learning through neural networks : introduction, theoretical properties, practical implementations (Tensorflow, PyTorch depending on acumen)
8. Generative and non-supervised learning: k-means

Pré-requis obligatoires

Probability (including conditional expectation ), statistics (undergraduate / L3 level), numerical analysis.

Bibliographie, lectures recommandées

See site of the course (site of the teacher); also see textbook by G. Turinici (cf. Amazon)

Enseignant responsable

GABRIEL TURINICI