Content
|
Lecture |
Lecturer |
Links |
w1 |
1. Introduction
|
Peter Bloem |
playlist pdf
|
2. Backpropagation
|
Peter Bloem |
playlist pdf
|
3. Convolutions
|
Michel Cochez |
playlist pdf
|
w2 |
3. Tools of the trade
|
Peter Bloem |
playlist pdf
|
5. Sequences
|
Peter Bloem, David Romero |
playlist pdf
|
w3 |
6. Latent Variable Models
|
Shujian Yu |
pdf
|
7. Unsupervised representation learning
|
Shujian Yu |
pdf
|
w4 |
8. Learning with graphs
|
Michael Cochez |
playlist pdf
|
9. Transformers and self-attention
|
Peter Bloem |
playlist pdf
|
w5 |
10. Reinforcement learning
|
Vincent Francois-Lavet |
pdf
|
w6 |
11. Diffusion models
|
Peter Bloem |
playlist pdf
|
12. Generalization
|
Shujian Yu |
pdf
|
Last year’s content
|
|
lecturer |
videos |
slides |
week 1 |
Introduction |
Jakub Tomczak |
A, B, C, D
|
pdf
|
|
Backpropagation |
Peter Bloem |
A, B, C, D
|
pdf |
|
Convolutional Neural Networks |
Michael Cochez |
A, B, C, D
|
pdf |
week 2 |
Sequential data |
Peter Bloem |
A, B, C, D, E*
|
pdf |
|
Tools of the trade |
Peter Bloem |
A, B, C, D
|
pdf |
week 3 |
Latent Variable Models (pPCA and VAE) |
Jakub Tomczak |
A, B, C
|
pdf |
|
GANs |
Jakub Tomczak |
A, B
|
pdf |
week 4 |
Learning with Graphs |
Michael Cochez |
playlist
|
pdf |
|
Transformers & self-attention |
Peter Bloem |
A, B, C |
pdf |
week 5 |
Reinforcement learning |
Emile van Krieken |
A, B, C
|
pdf |
|
Reinforcement learning (extra) |
Emile van Krieken |
A, B, C
|
pdf |
|
Autoregressive and Flow-based models |
Jakub Tomczak |
A, B, C
|
pdf |