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
July 2, 20199:30 am12:00 pm Richard Combes
July 2, 20191:30 pm4:30 pm Richard Combes
July 2, 2019
9:30 am - 12:00 pm - with Richard Combes - at
July 2, 2019
1:30 pm - 4:30 pm - with Richard Combes - at

Class Information

 Open: July 2, 2019

Class Trainer