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from IPython.display import display, Markdown
with open('readme.md', 'r') as fh:
content = fh.read()
display(Markdown(content))
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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 setup.sh 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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