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 30, 202110:50 am1:00 pm Richard Combes
June 30, 20212:00 pm4:10 pm Richard Combes
June 30, 2021
10:50 am - 1:00 pm - with Richard Combes - at
June 30, 2021
2:00 pm - 4:10 pm - with Richard Combes - at

Class Information

 Open: June 30, 2021

Class Trainer