Statistical learning

This course deals with the machine learning framework from a statistical point of view.
We will mainly focus on the supervised framework (regression and classification) and introduce some elements of the unsupervised framework through partitioning methods (clustering).
Beyond the modeling and theory aspects, the course will also cover some elements of optimization and implementation (sklearn, pytorch, etc.) of the introduced methods.

Be able to model a new learning problem in light of the objectives and methods available.

Linear model (HAX814X) / Software development (HAX712X) / Optimization (HAX706X)

 
 
Recommended Prerequisites: Inferential Statistics (HAX710X) and Nonparametric Estimation and Testing (HAX809X)

CC

Graded TP (code)

Project (report + presentation + code)

• Introduction to supervised learning; linear models.
• Cross-validation, logistic regression, discriminant analysis.
• Model selection and regularization methods.
• Performance measurement (multi-class: top-k, ROC curve AUC, etc.)
• Perceptron and stochastic gradient (descent).
• SVM
• Decision trees, Random forests and Boosting.
• Unsupervised learning (partitioning: KMEANS, Ward's method)
• Neural networks 

 
Hourly volumes:
CM: 21h
TD:
TP:
Field: