Lectures Syllabus 2026

Date Lecture Teacher Materials Reading / Extended Sources
18.02.2026 1. Recap of Machine Learning, Multi-Layer Perceptron AS slides Goodefellow 5.2-5.5
25.02.2026 2. Backpropagation AS slides Implicit Layers, Chapter 1
04.03.2026 3. History + CNN + DeepSet AS slides
11.03.2026 4. Training Deep Models (Init, Norm, etc.) AS slides
18.03.2026 5. Regularization Methods for NNs AS Slides
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: 2026/03/17 18:06 by shekhole