Seminar Schedule: Understanding Deep Learning (WS 25/26)
- Meetings: Mondays, weekly (20 Oct 2025 – 6 Feb 2026), 14.00-16.00, room OH12/1.054
- Winter break: 22 Dec 2025 – 2 Jan 2026
- Total students:
1911
| Date | Chapters | Pages (approx.) | Students | Notes |
|---|---|---|---|---|
| 20 Oct 2025 | Ch. 1–2: Introduction + Supervised Learning | – | Instructor | Intro lecture, organization, topic assignment |
| 27 Oct 2025 | Ch. 3–4: Shallow & Deep Neural Networks | 22 | P gr1, D gr2 | Fundamentals of network architectures |
| 3 Nov 2025 | Ch. 5–6: Loss Functions & Fitting Models | 30 | P gr3, D gr1 | Training objectives and optimization setup |
| Ch. 8–9: Performance & Regularization | 31 | P gr2, D gr3 | Evaluation metrics and regularization techniques | |
| Ch. 7: Gradients & Initialization | 15 | P gr6, D gr7 | Backpropagation & parameter initialization | |
| - | - | - | - | |
| 1 Dec 2025 | Ch. 12: Transformers | 25 | P gr5, D gr6 | Attention and transformer architectures |
| 8 Dec 2025 | Ch. 13: Graph Neural Networks | 21 | P gr8, D gr9 | Graph-based representations |
| 15 Dec 2025 | Ch. 10–11: CNNs & Residual Networks | 31 | P gr9, D gr5 | Convolutional structures and ResNets |
| 22 Dec – 2 Jan | Winter break | – | – | No meeting |
| 5 Jan 2026 | Ch. 18: Diffusion Models | 19 | P gr7, D gr4 | Modern generative diffusion approaches |
| 12 Jan 2026 | Ch. 19: Reinforcement Learning | 22 | P gr4, D gr8 | Policy gradients and deep RL |
Groups:
- gr1 Ponikarov Antonia
- gr2 Zoghlami Fadi, Ben Halima Ibrahim
- gr3 Filipiak Lucas, Romanovych Ivanna
- gr4 Marc Gläser
- gr5 Fadi Dalbah
- gr6 Gövert Noah
- gr7 Semler Alexander
- gr8 Vu Minh Nhat
- gr9 Pellecer Aisaiah