Course Material
This page hosts all lecture slides and supplementary reading materials for each topic covered in the course.
Topic 1: Introduction
Click to view slides in browser
Supplementary Material
Topic 2: PGM Representation
Click to view slides in browser
Supplementary Material
- Probabilistic Graphical Models (Textbook [4]), Chapters 3 & 4
- CS228 (Probabilistic Graphical Models) Notes
Topic 3: PGM Inference
Click to view slides in browser
Supplementary Material
- Probabilistic Machine Learning (Textbook [2]), Chapter 7.4
- Probabilistic Graphical Models (Textbook [4]), Chapter 9
- CS228 (Probabilistic Graphical Models) Notes
Topic 4: PGM Learning
Click to view slides in browser
Supplementary Material
- Probabilistic Graphical Models (Textbook [4]), Chapters 16 & 17
- A Note on the EM Algorithm
- CS228 (Probabilistic Graphical Models) Notes
Topic 5: Autoregressive Models
Click to view slides in browser
Supplementary Material
- Probabilistic Machine Learning (Textbook [2]), Chapter 22
- Harvard NLP Tutorial: The Annotated Transformer
- CSE599i: Neural Autoregressive Density Estimation (NADE)
- CSE599i: Transformers
- Self-Attention from Scratch (Sebastian Raschka)
- Introduction to Positional Encoding (Machine Learning Mastery)
Topic 6: Variational Autoencoder
Click to view slides in browser
Supplementary Material
- Probabilistic Machine Learning (Textbook [2]), Chapter 21
- CSE599i: VAE Notes
- CS236 (Deep Generative Models): VAE Notes