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Kernel methods for machine learning
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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