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 15
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 1 - Kernels, RKHS, examples
Description: Introduction to kernels and related notions.
Slides: 1-77
Materials: [Video 1] [Video 2]
Additional Materials:
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Date: Jan 22
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 2 - Kernel trick, Representer theorem, Kernel Ridge regression
Description: Main tools for kernel methods
Slides: 53-100
Materials: [Video 1] [Video 2] [Video 3]
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Date: Jan 29
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 3 - Logistic regression, Large-margin classifiers and SVMs
Description: Application of kernel methods to classification
Slides: 101-162
Materials: [Video 1]
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Date: Feb 05
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 4 - Unsupervised learning, kernel PCA
Description: Kernel methods for unsupervised learning.
Slides: 222-279
Materials: [Video 1] [Video 2]
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Date: Feb 12
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 5 - Unsupervised learning, K-means, CCA
Description: Kernel methods for unsupervised learning.
Slides: 179-199
Materials: [Video 2]
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Date: Feb 19
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 6 - Green, Mercer, Herglotz, Bochner and friends
Description: Some kernel theory. -
Date: Feb 26
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 7 - Kernels for graphs, kernels on graphs
Description: Some applications of kernel methods to graph structured data. -
Date: Mar 05
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 8 - Kernel Mean Embeddings of probability distributions
Description: Representing probability distributions using kernels -
Date: Mar 12
Room: 16bis rue de l'Estrapade, 75005 Paris
Material: Lecture 9 - Generalization of Kernel Ridge Regression
Description: Scaling up kernel methods to large problems