Machine learning

Ects : 5

Enseignant responsable :

Volume horaire : 39

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.

Coefficient : cf. CC

Compétence 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).

Bibliographie, lectures recommandées

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