Reproducing kernel Hilbert spaces in Machine LearningArthur Gretton (with Dimitri Meunier) |
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This course represents half of Advanced Topics in Machine Learning (COMP 0083) from the UCL CS MSc on Machine Learning. The other half is a course on Convex Optimization.
This page will contain slides and detailed notes for the kernel part of the course. The assignment may also be found here (at the bottom of the page). Note that the slides will be updated as the course progresses, and I modify them to answer questions I get in the classes. I'll put the date of last update next to each document - be sure to get the latest one. Let me know if you find errors.
There are sets of practice exercises and solutions further down the page (after the slides).
For questions on the course material, please email Dimitri Meunier.
Slides and notes
Lectures 1 and 2 slides and notes, last modified 13 Oct 2023
Lecture 3 slides (notes same as for lectures 1 and 2), last modified 20 Oct 2021
Lecture 4,5 slides and notes, last modified 12 December 2022
Lecture 6,7 slides and notes (notes same as lecture 4), last modified 14 Nov 2023
Lecture 7 slides and notes (notes same as lecture 4), last modified 07 Nov 2018
Lecture 8 slides, last modified 01 Dec 2021
Lecture 9 slides and notes, last modified 01 Dec 2021
Lecture 10 Slides, last modified 19 Dec 2023
Theory lectures Slides 1, Slides 2 , and notes, last modified 20 Mar 2013
Supplementary lecture slides, last modified 22 Mar 2012
Assignment
The assignment (first part due on Friday Nov 24, 2023). You will need this extract on incomplete Cholesky (scanned from Shawe-Taylor and Cristianini, Kernel Methods for Pattern Analysis). Last modified 13 November 2023.
Practice exercises and solutions
The exercises are taken from exams in previous years, with minor modifications. Worked solutions are provided. These will be posted after the course begins.