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