Lectures program
| Title | Link | References | Day |
|---|---|---|---|
| 0. Course logistic | slides | 02.03.2026 | |
| 1. Introduction + History of AI | slides | Chap. 1 DL book | 02.03.2026 |
| 2. Linear Algebra | slides | Chap. 2 DL book + MML book | 03.03.2026 |
| 3. Probability | slides | Chap. 3 DL book + Chap1.2, 2.3 PRML book | 09.03.2026 |
| 4. Information theory | slides | Chap. 3 DL book + Chap1.2, 2.3 PRML book | 10.03.2026 |
| 5. Calculus | slides | Chap. 4 DL book | 11.03.2026 |
| 6. Machine Learning basics | slides | Chap. 5 DL book | 16.03.2026 |
| 7. 💻 Introduction to Python | see material + notebook | 17.03.2026 | |
| 8. Unsupervised Learning + k-Means | slides | Chap. 14 ESLII book | 18.03.2026 |
| 9. Hierachical clustering + DBSCAN | slides | Chap. 14 ESLII book | 23.03.2026 |
| 10. PCA / ICA | slides | Chap. 14 ESLII + Chap 12 PRML | 24.03.2026 |
| 11. 💻 scikit-learn clustering | notebook | 25.03.2026 | |
| 12. Manifold learning + Spectral clustering | slides | Chap. 14 ESLII | 30.03.2026 |
| 13. Clustering evaluation | slides | Chap 14 ESLII | 31.03.2026 |
| 14. 💻 scikit-learn clustering pt. II | notebook | 01.04.2026 | |
| 15. Supervised Learning + Linear Regression pt. 1 | slides | Chap 2.1 ESLII - Chap 3.1 PRML | 15.04.2026 |
| 16. Supervised Learning + Linear Regression pt. 2 | slides | Chap 2.1 ESLII - Chap 3.1 PRML | 20.04.2026 |
| 17. Model Selection + Regularization | slides | Chap 1.3, 3.2 PRML - Chap 3.4, 7 ESLII | 21.04.2026 |
| 18. 💻 Principal Component Analysis | notebook | 22.04.2026 | |
| 19. Linear classification + kNN + LDA | slides | Chap 2.5.2, 4 PRML - Chap 2.3, 4 ESLII | 27.04.2026 |
| 20. Linear classification + kNN + LDA (pt. 2) | slides | Chap 2.5.2, 4 PRML - Chap 2.3, 4 ESLII | 28.04.2026 |
| 21. Ensemble + Decision Trees + Random Forest | slides | Chap 8.7, 9.2, 10.2, 15 ESLII | 29.04.2026 |
| 22. SVM + Kernels | slides | Chap. 6, 7 PRML | 04.05.2026 |
| 23. Preprocessing + Feature selection + Testing | slides | 05.05.2026 | |
| 24. Feature interpretation + recap | slides | 06.05.2026 | |
| 25. Neural Networks Intro | slides | Chap 6 DL | 12.05.2026 |
| 26. Neural Networks Optimization | slides | Chap 6 DL | 13.05.2026 |
| 27. 💻 Classification + Neural Networks | notebook-classification | 18.05.2026 | |
| 28. CNN | slides | Chap. 9 DL | 19.05.2026 |
| 29. RNN + Autoencoder + GAN | slides RNN slides-ae | Chap 10 DL, 12.4 DL | 20.05.2026 |
| 30. 💻 Neural network intro | notebook-nn | 22.05.2026 | |
| 31. Large Language Models | slides | 25.05.2026 | |
| 32. Machine Learning and Neuroscience | slides | 26.05.2026 | |
| 33. 💻 Neural network intro + pytorch | notebook | ||
| 34. 🖥️ Machine learning project | notebook data solution |
Datasets for the project
1) EEG features to predict psychiatric disorders dataset paper
3) Bilingualism and the brain dataset paper.
Bonus track
| Title | Link | References | Day |
|---|---|---|---|
| 12. Gaussian Mixture Models + EM + HMM | slides | Chap. 13 + 9 PRML | |
| 10. 💻 Python algebra + numpy + data visualization | notebook | ||
| 31. 💻 pytorch | notebook | ||
| 32. Dataviz | slides | ||
| 32. Machine Learning and Neuroscience | slides | ||
| 30. Autoencoder + GAN | slides | ||
| 12. 💻 Intro to python pt. II | see material + notebook |