| 1 |
Course information |
No reading |
PDF |
|
| 2 |
Introduction to learning theory |
UML chapter 02, 03 |
PDF |
Code |
| 3 |
Linear predictors |
UML chapter 09 |
PDF |
Code |
| 4 |
Model complexity |
UML chapter 05 |
PDF |
Code |
| 5 |
VC dimension |
UML chapter 05, 06 |
PDF |
|
| 6 |
SVMs and kernel methods |
UML chapter 15, 16 |
PDF |
Demo |
| 7 |
Model selection and validation |
UML chapter 11 |
PDF |
|
| 8 |
Neural networks |
UML chapter 14 |
PDF |
Demo |
| 9 |
Optimization methods |
UML chapter 20 |
PDF |
|
| 10 |
CNNs and RNNs |
DL chapter 09, 10 |
PDF |
|
| 11 |
Generative models |
|
PDF |
|
| 12 |
Clustering |
|
PDF |
|
| 13 |
Dimensionality reduction |
|
PDF |
|
| 14 |
Final project presentation |
No reading |
|
|