Machine Learning 2

This is the website for the Machine Learning 2 course (TC1) of Master 1 AI (2020-2021).


The objective of this course is to provide the essential bases in machine learning: the main families of models and the associated algorithms (inference and learning). This course covers:

  • Optimization algorithms for machine learning,
  • Sparsity,
  • Bayesian Machine Learning,
  • Structured Prediction.

This course follows the course [TC0] Machine Learning 1: it is expected that students know the basis of machine learning and the main classifiers (linear models, SVM). We will try to formalize and generalize different models to gain a deeper understanding of the principles underlying the learning process. As such, students following this course will have the theoretical foundation to understand modern machine learning, including deep learning models.

Grading Scheme

  • 25%: Lab exercises
  • 75%: Exam


You can contact me at caio.corro@u-psud.fr, either in French or English, with a subject starting with [TC1]. Please, do not worry about typos or not being overly formal enough (just treat your instructors and colleagues with the same respect you would like to be treated).

WARNING: Each mail must discuss at most one point. Don’t send e-mails to several of my addresses. Thank you.