Statistical learning

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

Be able to model a new learning problem based on the objectives and available methods.

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

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

CC

Graded assignment (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 

 
Hours:
CM: 21 hours
TD:
TP:
Fieldwork: