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 This course deals with: Topic 1: Neural networks : Basic multi-layer networks / Convolutional networks for image data / Recurrent networks for sequence data / Generative neural network models Topic 2: Kernel methods : Theory of RKHS and kernels / Supervised learning with kernsl / Unsupervised learning with kernels / Kernels for structured data / Kernels for generative models It is composed of 18 hours lectures. Evaluation : There will be a written homework with theoretical exercises. In addition the students participate in a data challenge in which they implement a machine learning method of choice to solve a prediction problem on a given dataset. Both elements contribute equally to the final grade. See course website.

Learning Achievement

Competence

Course prerequisites

Grading Philosophy

Course schedule

Course type

Lecture

Online Course Requirement

Instructor

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