Course Overview

Statistical learning is about the construction and study of systems that can automatically learn from data. With the emergence of massive datasets commonly encountered today, the need for powerful machine learning is of acute importance. Examples of successful applications include effective web search, anti-spam software, computer vision, robotics, practical speech recognition, and a deeper understanding of the human genome. This course gives an introduction to this exciting field, with a strong focus on kernels methods and neural network models as a versatile tools to represent data

http://ensimag.grenoble-inp.fr/fr/formation/advanced-learning-models-wmms536i-1

Learning Achievement

Competence

Introduction to statistical learning theory and kernel-based methods.

Course prerequisites

Fundamental notions in linear algebra and statistics. Basic programming skills to implement a machine method of choice encountered in the course from scratch

Grading Philosophy

Final exam

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

Julien Mairal - Xavier Alameda

Other information

Course content can evolve at any time before the start of the course. It is strongly recommended to discuss with the course contact about the detailed program.

Please consider the following deadlines for inbound mobility to Grenoble:
- April 1st, 2020 for Full Year (September to June) and Fall Semester (September to January) intake ;
- September 1st, 2020 for Spring Semester intake (February – June).

Site for Inquiry