Lectures
You can download the slides for all lectures here! Each lecture corresponds to a range of slides. Slides are frequently updated. Please let us know if you spot typos!
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Date: Jan 07
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 1 - Kernels, RKHS, kernel trick
Description: Introduction to kernels and related notions.
Slides: 1-70
Materials: [Video 1] [Video 2] [Video 3] [Video 4]
Additional Materials:
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Date: Jan 21
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 2 - Representer theorem, Kernel Ridge and Logistic regression, Large-margin classifiers
Description: Main tools for kernel methods
Slides: 71-129
Materials: [Video 1] [Video 2] [Video 3]
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Date: Jan 28
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 3 - Support Vector Machines and Unsupervised learning
Description: SVMs and Unsupervised learning
Slides: 143-199
Materials: [Video 1] [Video 2] [Video 3]
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Date: Feb 04
Room: online (check your emails for the link)
Material: Lecture 4 - Kernels for graphs
Description: Kernel methods for unsupervised learning.
Slides: 444-493
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Date: Feb 18
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 5 - Green, Mercer, Herglotz, Bochner and friends
Description: Kernel methods for unsupervised learning.
Slides: 202-259
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Date: Feb 25
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 6 -
Description: Some kernel theory.
Slides: -
Additional Videos:
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Date: Mar 11
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 7 -
Description: Representing probability distributions using kernels -
Date: Mar 18
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 8 -
Description: Some applications of kernel methods to graph structured data.