Kernel Methods in Machine Learning

African Master's in Machine Intelligence (AMMI), May 2020

Table of contents

• Introduction
• Course Slides
• Requirements
• Data Challenge
• AMMI 2019
• Teaching Staff

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.)