Kernel Methods in Machine Learning
African Master's in Machine Intelligence (AMMI), May 2020
Table of contents
Introduction
This course covers basic concepts in machine learning in high dimension, and the importance of regularization. We study in detail high-dimensional linear models regularized by the Euclidean norm, including ridge regression, ridge logistic regression and support vector machines. We then show how positive definite kernels allows to transform these linear models into rich nonlinear models, usable even for non-vectorial data such as strings and graphs, and convenient for integrating heterogeneous data.
Course Slides
Slides for the course can be found here: Slides
Requirements
For practical sessions, a working jupyter notebook setup is required. Course material will be done in python.
Data Challenge
See the dedicated kaggle
AMMI 2019
For practice exercises and quizzes, please check out last year's course material
Teaching Staff
- Jean-Philippe Vert (Prof.)
- Julien Mairal (Prof.)
- Romain Menegaux (T.A.)