Lectures Syllabus 2026

Date Lecture Teacher Materials Reading
18.02.2026 1. Recap of Machine Learning, Multi-Layer Perceptron AS slides Goodefellow 5.2-5.5
25.02.2026 2. Backpropagation AS
04.03.2026 3. History + CNN + DeepSet AS
11.03.2026 4. Training Deep Models (Init, Norm, etc.) AS
18.03.2026 5. Regularization Methods for NNs AS
25.03.2026 6. Stochastic Gradient Descent (SGD) AS
01.04.2026 7. Self-Attention, Transformers GT
08.04.2026 8. Adaptive Optimization Methods AS
15.04.2026 9. Learning Representations I: Word Vectors, Metric Learning AS
22.04.2026 10. Adversarial Patterns, Biases, Security GT
29.04.2026 11. Graph Neural Networks GT
06.05.2026 12. Learning Representations II: VAE (+ diffusion) AS
13.05.2026 — Rector's day —
20.05.2026 13. TBA (tentatively Autoregressive, SSM) AS
courses/bev033dla/lectures.txt · Last modified: 2026/02/18 13:22 by shekhole