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 slides
01.04.2026 7. Self-Attention, Transformers GT slides
08.04.2026 8. Adaptive Optimization Methods AS slides
15.04.2026 9. Learning Representations I: Word Vectors, Metric Learning AS slides
22.04.2026 10. Graph Neural Networks GT slides Graph affinity, Laplacian, etc.
29.04.2026 11. Responsible AI GT slides
06.05.2026 12. Learning Representations II: Autoencoders AS slides
13.05.2026 — Rector's day —
20.05.2026 13. TBA (tentatively Autoregressive, SSM) AS
courses/bev033dla/lectures.txt · Last modified: 2026/05/05 18:28 by shekhole