Notes on :: Probabilistic Machine Learning : Advanced Topics

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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.

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Table of Contents

• 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