Spring 2019: Kernel Methods in Machine Learning

21 Jan 2019
Final project prediction performance results.

Master course at African Master’s in Machine Intelligence (AMMI).

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.

Instructors:

  • Jean-Philippe Vert (Prof.)
  • Yunlong Jiao (T.A.)

More on the course website.

Machine Learning