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Accueil > Formations > Master MVA > Présentation des cours

Kernel methods for machine learning

Lecturers : Julien Mairal (INRIA)- Jean-Philippe Vert (ENS Paris & Mines ParisTech)

Objective of the course:

To present theoretical foundations and applications of kernel methods in machine learning

Topics :


  • 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

Prerequisites :

Introductory course on probability, Introductory course on computer programming and data structures.

Organization of courses :

8 lectures, 3 hours each
1 final project (data challenge)

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.

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