Schedule

Event

Date

Description

Course Material
Lecture

01/11/2023
Wednesday

Lecture 1 - Kernels, RKHS, examples
[Video 1] [Video 2]
Additional Materials:
- Uniqueness of the RKHS
- Aronszajn’s theorem
Assignment

01/11/2023
Wednesday

Assignment #1 released!

[Assignment #1]
Lecture

01/25/2023
Wednesday

Lecture 2 - Smoothness functional, Kernel trick, Representer theorem
[Video 1] [Video 2] [Video 3]
Lecture

02/01/2023
Wednesday

Lecture 3 - Kernel ridge and logistic regression
[Video 1]
Due

02/01/2023 15:00
Wednesday

Assignment #1 due
Assignment

02/04/2023
Saturday

Kaggle challenge released!

[Kaggle challenge ]
Lecture

02/08/2023
Wednesday

Lecture 4 - Large-margin classifiers, SVMs
[Video 1] [Video 2]
Additional Materials:
- Bartlett et al. 2003
Assignment

02/08/2023
Wednesday

Assignment #2 released!

[Assignment #2]
Lecture

02/22/2023
Wednesday

Lecture 5 - Unsupervised learning, kernel PCA, K-means, CCA
[Video 1] [Video 2]
Lecture

03/01/2023
Wednesday

Lecture 6 - Green, Mercer, Herglotz, Bochner and friends
[Video 1] [Video 2] [Video 3]
Additional Videos:
- Statistical convergence rates
- Shorter version of Green, Mercer and Bochner
Due

03/01/2023 15:00
Wednesday

Assignment #2 due
Assignment

03/01/2023
Wednesday

Assignment #3 released!

[Assignment #3]
Lecture

03/08/2023
Wednesday

Lecture 7 - Kernels for graphs, kernels on graphs
[Video 1] [Video 2]
Additional Videos:
- Kernels for biologial sequences
Lecture

03/15/2023
Wednesday

Lecture 8 - Kernel Mean Embeddings of probability distributions
[Video 1] [Video 2]
Additional Videos:
- kernels for probabilistic models
Due

03/15/2023 15:00
Wednesday

Assignment #3 due
Lecture

03/22/2023
Wednesday

Lecture 9 - Deep kernel machines and large-scale learning with kernels
Exam

03/29/2023 13:30
Wednesday

Written Exam
Due

04/12/2023 15:00
Wednesday

Kaggle challenge ends