Past Years
2024
| Week | Date | Lecturer | Topic & Materials |
|---|---|---|---|
| 1 | 22.2.2024+L | Miroslav Čepek | Lecture: Introduction & Repeatable ML Projects - MLOps Supplement - Weight & Biases Tutorial |
| 2 | 29.2.2024 | Petr Šimánek | Lecture: Optimisation in Deep Learning |
| 3 | 7.3.2024+L | Zdeněk Buk | Lecture: ML in modeling and control |
| 4 | 14.3.2024 | Rodrigo Da Silva Alves | Lecture: Recommender Systems (1) |
| 5 | 21.3.2024+L | Rodrigo Da Silva Alves | Lecture: Recommender Systems (2) |
| 6 | 28.3.2024 | Vojtěch Rybář | Lecture: Interpretable and Explainable Models |
| 7 | 4.4.2024+L | Vojtěch Rybář | Lecture: Causal Machine Learning |
| 8 | 11.4.2024 | Alexander Kovalenko | Lecture: Advanced Image Processing |
| 9 | 18.4.2024+L | Alexander Kovalenko | Lecture: Nature Inspired Deep Learning |
| 10 | 25.4.2024 | Petr Šimánek | Lecture: Meta and Continual Learning |
| 11 | 2.5.2024+L | EXCEPTION | No lecture - Wednesday’s schedule. |
| 12 | 9.4.2024 | Petr Šimánek | Lecture: Physics Informed ML |
| 13 | 16.5.2024+L | Miroslav Čepek | Lecture: Large Language Models == 2023 [%header,cols="1,2,1,5"] |
| Week | Date | Lecturer | Topic & Materials | 1 | 23.2.2023 | Petr Šimánek | Introduction, Optimisation in Deep Learning & Optimisation | 2 | 2.3.2023 | Petr Šimánek & Miroslav Čepek | Optimisation in Deep Learning & Repeatable ML Projects - MLOps | 3 | 9.3.2023 | Rodrigo Da Silva Alves | Recommender Systems: Intro | 4 | 16.3.2023 | Rodrigo Da Silva Alves | Recommender Systems: Modern Methods | 5 | 23.3.2023 | Alexander Kovalenko | Advanced Image Processing | 6 | 30.3.2023 | Zdeněk Buk | ML in modeling and control | 7 | 6.4.2023 | — | EXCEPTION - Monday’s schedule | 8 | 13.4.2023 | Miroslav Čepek | Time Series Modeling | 9 | 20.4.2023 | Petr Šimánek | Meta and Continual Learning | 10 | 27.4.2023 | Vojtěch Rybář | Interpretable and Explainable Models | 11 | 4.5.2023 | Vojtěch Rybář | Causal Machine Learning | 12 | 11.5.2023 | Petr Šimánek | Physics Informed ML | 13 | 18.5.2023 | Petr Šimánek | AI Alignment
| [%header,cols="1,2,1,5"] |
| Week | Date | Lecturer | Topic & Materials | 1 | 2.3.2023 | Petr Šimánek | Optimisation in Deep Learning (& link). Homework: Use the previous link, train one network for 50 epochs with three different methods: AdamW, LION and one 2nd order method. Compare the loss, accuracy and batch gradient variance (from BackPACK) for each method. Implement the 2nd-order method using BackPACK (e.g. Diagonal Gauss-Newton Second order optimizer). Deadline: 21.3.2023. Send the resulting colab notebook to petr.simanek@fit.cvut.cz. | 2 | 16.3.2023 | Rodrigo Da Silva Alves | Recommender Systems: The instructions for the tutorial and homework are in notebook you can find Recommender Systems: here. | 3 | 30.3.2023 | Zdeněk Buk | ML in modeling and control | 4 | 20.4.2023 | Miroslav Čepek | Advanced Image Processing - Denoising Diffusion Model
| 5 | 27.4.2023 | Vojtěch Rybář | Interpretable and Explainable Models | 6 | 11.5.2023 | | Work on project