The primary objective of this course is to master fundamental machine learning concepts, such as loss functions, Bayes risk, empirical risk, VC dimension, regression, and classification. The course also includes hands-on practice with some of the most powerful methods, often referred to as "black boxes," including neural networks, Hidden Markov Models, and SVMs. By also studying methods for evaluating learning quality, the course will contribute to the development of students' analytical skills.
- Enseignant: asmaa ourdighi