1 |
Course overview and introduction to Machine Learning |
- |
2 |
Linear algebra refresher |
- |
3 |
Probability refresher |
- |
4 |
Gradient-based optimization |
link |
5 |
Linear and Logistic regression |
link |
6 |
Neural Networks: perceptron, activation functions |
link1 |
7 |
Neural Networks: backpropagation, initialization, and loss functions |
- |
8 |
Best practices in training of Machine Learning models |
- |
9 |
Advanced solvers: momentum, RMSProp, Adam, greedy training |
- |
10 |
UQ in Neural Networks and Mixture Density Networks |
link1 link2 |
11 |
Introduction to CNNs |
- |
12 |
CNNs Popular Architectutues |
link |
13 |
Sequence modelling: basic principles |
|
14 |
Sequence modelling: architectures |
link |
15 |
Dimensionality reduction |
|
16 |
Generative modelling and VAEs reduction |
|
17 |
GANs |
|
18 |
Scientific ML and PINNs |
link |
19 |
Deep learning for Inverse Problems |
|
20 |
Invertible Neural Networks |
|
21 |
Implicit Neural Networks |
|