Introduction to Machine Learning


This course presents the fundamentals of statistical learning theory, from fundamental limits, to algorithms, their analysis and practical implementation. Important concepts such as Vapnik-Chervonenkis theory, Empirical Risk Minimization, Stochastic Gradient Descent algorithms and Kernel methods will be presented in details. A lab session will enable students to apply the theoretical contents to data.

Class Timetable

 Start TimeEnd TimeProfessor
June 28, 20229:30 am12:00 pm Richard Combes
June 28, 20221:30 pm4:00 pm Richard Combes
June 28, 2022
9:30 am - 12:00 pm - with Richard Combes - at
June 28, 2022
1:30 pm - 4:00 pm - with Richard Combes - at

Class Information

 Open: June 28, 2022

Class Trainer