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What is this course about? I recently started a reinforcement learning project where the reward function of the RL learner was a bayesian model. Of course...I knew almost nothing about bayesian statistics before building out this RL learner! So, I spent a lot of time reading and trying to implement various online tutorials and research papers. This course is a collection of (mostly) other people's tutorials, but refactored to work together and provide a more holistic view of bayesian statistics than any one of these myriad of tutorials did originally. I've rewritten extensive portions of many of these tutorials and I apologize for any errors I may have accidentally introduced. I try to provide links or references to any and all resources I borrowed from to create this course in the sections where I reference said material.

All of the content is stored in Jupyter Notebooks. I recommend downloading the content locally. This will let you play with the code and actually run the examples for yourself. For best practices, I also recommend using a conda virtual environment to manage dependancies (take a look at the file if you need help setting up your env and/or check out the documentation).

Table of Contents:

Part One:

  • Who is this lecture for?
  • What is statistics?
  • The Frequentist Philosophy
  • The Bayesian Philosophy
  • Author's Note: Taking Sides

Part Two:

  • Bayes: Conjugacy
  • Bayes: Metropolis Hastings Sampling
  • Bayes: My Friends Edward, Stan, and Pymc3

Part Three:

  • Bayes: Hyper Priors
  • Bayesians vs Frequentists (Student's T Test)
  • Bayesians vs Frequentists (A/B Testing)
  • Bayes: Gibbs Sampling

Part Four:

  • Bayes: Linear Regression Part One (simple)
  • Bayes: Linear Regression Part Two (regularization)
  • Bayes: Linear Regression Part Three (robust)

Part Five:

  • Bayes: Linear Regression Four (multi-level)
  • Bayes: Linear Regression Four and Three Quarters (escaping the funnel)
  • Samplers: Beyond Metropolis Hastings

Part Six:

  • Bayes: Linear Regression with a Time Component (includes neural net with time component)

Part Seven:

  • Bayes: Variational Inference

Part Eight:

  • Bayes: Training Neural Nets Part One
  • Bayes: Training Neural Nets Part Two
  • Bayes: Training Neural Nets Part Three
  • Bayes: Reguarization
  • Bayes: Time Series Neural Nets

Part Nine:

  • Mixture Models Part One
  • Mixture Models Part Two
  • Mixture Models Part Three
  • Mixture Models Part Four

Part Ten:

  • Gaussian Processes
  • Parting Words

Part Eleven:

  • Naive Bayes
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