Personal notes on Probabilistic ML: Advanced Topics by Kevin P. Murphy.
Summary is written as short bullet points w/ relevant equations in a *.ipynb
markdown.
- I Fundamentals
- Chapter 01: Introduction
- Chapter 02: Probability
- Chapter 03: Statistics
- Chapter 04: Graphical models
- Chapter 05: Information theory
- Chapter 06: Optimization
- Chapter 07: Inference algoriths: an overview
- II Inference
- Chapter 08: Gaussian filtering and smoothing
- Chapter 09: Message passing algorithms
- Chapter 10: Variational Inference
- Chapter 11: Monte Carlo methods
- Chapter 12: Markov chain Monte Carlo
- Chapter 13: Sequential Monte Carlo
- III Prediction
- Chapter 14: Predictive models: an overview
- Chapter 15: Generalized linear models
- Chapter 16: Deep neural networks
- Chapter 17: Bayesian neural networks
- Chapter 18: Gaussian processes
- Chapter 19: Beyond the iid assumption
- IV Generation
- Chapter 20: Generative models: an overview
- Chapter 21: Variational autoencoders
- Chapter 22: Autoregressive models
- Chapter 23: Normalizing flows
- Chapter 24: Energy-based models
- Chapter 25: Diffusion models
- Chapter 26: Generative adversarial networks
- V Discovery
- Chapter 27: Discovery methods: an overview
- Chapter 28: Latent factor models
- Chapter 29: State-space models
- Chapter 30: Graph lerning
- Chapter 31: Non-parametric Bayesian models
- Chapter 32: Representation learning
- Chapter 33: Interpretability
- VI Action
- Chapter 34: Decision making under uncertainty
- Chapter 35: Reinforcement learning
- Chapter 36: Causality