Prè-requis
Introductory course on probability, Introductory course on computer programming and data structures.
Objectif du cours
To present theoretical foundations and applications of kernel methods in machine learning
Organisation des séances
8 lectures, 3 hours each
1 final project (data challenge)
Mode de validation
Homeworks (50%) and data challenge (50%)
Références
- N. Aronszajn. Theory of reproducing kernels. Transactions of the American Mathematical Society, 68:337-404, 1950.
- C. Berg, J.P.R. Christensen et P. Ressel. Harmonic analysis on semi-groups. Springer, 1994.
- N. Cristianini and J. Shawe-Taylor. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
Thèmes abordés
- Positive definite kernels
- Reproducing kernel Hilbert spaces
- Kernel trick
- Representer theorem
- Kernel ridge regression
- Support vector machines
- Kernel k-means and Spectral clustering
- Kernel PCA
- Kernel CCA
- Mercer kernels
- Kernel for strings
- Kernels for graphs
- Kernels on graphs
- Multiple kernel learning
- Large-scale learning with kernels
- Deep learning with kernels
- Kernel mean embedding
Les intervenants
Julien Mairal
(INRIA)
Michael Arbel
(INRIA)