Introduction to Inference and Learning

Many of my subscribers have asked for some resources to help get them on a path for better understanding with regards to inference and learning. As many individuals have various learning styles there are both reading and video (I would recommend both).

  • Book: Murphy — Chapter 1 — Introduction
  • Book: Bishop — Chapter 1 — Introduction

Books mentioned above:

Machine Learning: A Probabilistic Perspective Kevin P. Murphy, MIT Press, 2012.

Pattern Recognition and Machine Learning Christopher M. Bishop, Springer, 2006. An excellent and affordable book on machine learning, with a Bayesian focus. It covers fewer topics than the Murphy book, but goes into more depth on the topics it covers.

If you have resources that you think that I missed, please let me know. If there is a resource that you particularly enjoyed I would like to hear from you as well.

Simple Discrete Models

For those of you interested in learning a bit more about discrete models, below are some fantastic resources to help you on your journey:

  • Book: Murphy — Chapter 2 — Probability
  • Book: Murphy — Chapter 3 — Generative Models for Discrete Data
  • Book: Bishop — Chapter 2, Sections 2.1-2.2 — Probability Distributions
  • Book: MacKay — Chapter 2 — Probability, Entropy, and Inference
  • Book: MacKay — Chapter 3 — More About Inference
  • Book: Mackay — Chapter 23 — Useful Probability Distributions

 

Book reference for above:

Machine Learning: A Probabilistic Perspective Kevin P. Murphy, MIT Press, 2012.

Pattern Recognition and Machine Learning Christopher M. Bishop, Springer, 2006. An excellent and affordable book on machine learning, with a Bayesian focus. It covers fewer topics than the Murphy book, but goes into more depth on the topics it covers.

David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press.