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,
- 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.
- 25%: Lab exercises
- 75%: Exam
You can contact me at firstname.lastname@example.org, 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.