kernel methods

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1. 1▶Lecture 12a of kernel methods: Kernels for graphsKernel methods in machine learning - MVA2021Notes
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2. 2▶Lecture 11c of kernel methods: Convergence rates of kernel ridge regression for Mercer kernelsKernel methods in machine learning - MVA2021Notes
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3. 3▶Lecture 11d of kernel methods: Translation invariant kernels, Herglotz and Bochner's theoremsKernel methods in machine learning - MVA2021Notes
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4. 4▶Lecture 10 on kernel methods: kernel K-means, spectral clustering, kernel CCAKernel methods in machine learning - MVA2021Notes
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5. 5▶Lecture 9 on kernel methods: kernel PCAKernel methods in machine learning - MVA2021Notes
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6. 6▶Lecture 8 on kernel methods: Supervised learning, support vector machines (SVM)Kernel methods in machine learning - MVA2021Notes
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7. 7▶Lecture 7 on kernel methods: Supervised learning, large-margin classifiers, a bit of learning theoryKernel methods in machine learning - MVA2021Notes
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8. 8▶Lecture 6 on kernel methods: Supervised learning, kernel ridge and logistic regressionKernel methods in machine learning - MVA2021Notes
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9. 9▶Lecture 5 on kernel methods: Representer theoremKernel methods in machine learning - MVA2021Notes
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10. 10▶Lecture 4 on kernel methods: Kernel TrickKernel methods in machine learning - MVA2021Notes
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11. 11▶What is Machine Learning - Bernhard Schölkopf - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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12. 12▶Learning Digital Humans by Capturing Real Ones - Michael Black - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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13. 13▶Implicit Generative Models - Ilya Tolstikhin - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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14. 14▶Reinforcement Learning - Jan Peters - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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15. 15▶Marrying Graphical Models & Deep Learning - Max Welling - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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16. 16▶Robot Learning - Stefan Schaal - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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17. 17▶Distributed Architectures Part 1 - Michael Jordan - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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18. 18▶Distributed Architectures Part 2 - Michael Jordan - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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19. 19▶Distributed Architectures Part 3 - Michael Jordan - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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20. 20▶Kernel Methods Part 2 - Bharath Sriperumbudur - MLSS 2017Machine Learning Summer School 2017 TübingenNotes
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21. 21▶Deep Learning -- Yoshua Bengio (Part 1)All TalksNotes
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22. 22▶Deep Learning -- Yoshua Bengio (Part 3)All TalksNotes
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23. 23▶Kernel methods and computational biology -- Jean-Philippe Vert (Part 1)All TalksNotes
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24. 24▶Introduction to Machine Learning -- Neil Lawrence (Part 1)All TalksNotes
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25. 25▶Probabilistic Modelling -- Iain Murray (Part 2)All TalksNotes
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26. 26▶Big Data and Large Scale Inference -- Amr Ahmed (Part 2)All TalksNotes
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27. 27▶Hamiltonian Monte Carlo and Stan -- Michael Betancourt (Part 2)All TalksNotes
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28. 28▶What is Machine Learning: A Probabilistic Perspective -- Neil Lawrence (Part 2)All TalksNotes
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29. 29▶Deep Learning -- Yoshua Bengio (Part 2)All TalksNotes
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30. 30▶Probabilistic Modelling -- Iain Murray (Part 3)All TalksNotes
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