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Kernel methods in machine learning - MVA2021

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Lecture 12a of kernel methods: Kernels for graphs

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Lecture 11c of kernel methods: Convergence rates of kernel ridge regression for Mercer kernels

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Lecture 11d of kernel methods: Translation invariant kernels, Herglotz and Bochner's theorems

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Lecture 10 on kernel methods: kernel K-means, spectral clustering, kernel CCA

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Lecture 9 on kernel methods: kernel PCA

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Lecture 8 on kernel methods: Supervised learning, support vector machines (SVM)

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Lecture 7 on kernel methods: Supervised learning, large-margin classifiers, a bit of learning theory

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Lecture 6 on kernel methods: Supervised learning, kernel ridge and logistic regression

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Lecture 5 on kernel methods: Representer theorem

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Lecture 4 on kernel methods: Kernel Trick

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