Learning resources

This page gathers materials for different topics that interest me. This page is not a comprehensive list, but more of a curation of resources I found very useful for anyone looking to kickstart or broaden her\his knowledge on a subject. Personally, I hope to gain a good level of understanding of these topics in my PhD, related or unrelated to my ongoing research. And I’ve found it usefull to have one place where I keep recommendations for other friends who would like to get into something new, so might as well share in public. Happy for any suggestions/additions


  1. Bayesian modeling \ Probabilistic programming
  2. Reinforcement Learning basics
  3. Causal Inference
  4. Survival analysis
  5. Systems Medicine

1. Bayesian modeling \ Probabilistic programming

The trully awesome Statistical Rethinking book and lectures by Richard Mcelreath. Can’t recommend this enough. [course page], [videos], [Python code]

PyMC3 - Python library of choice. Great community, check out learning materials [here], and good intro videos from the latest PyMCon [videos]

Stan has a nice collection of very informative [case studies]

Bayesian workflow [paper]

Andrew Gelman’s [blog] always has something interesting to read.

2. Reinforcement Learning basics

Stanford cs234 course [course page] [videos]

Full free book by Sutton & Barto [book]
and Python implementation of the whole book [code]

David Silver’s RL course [videos]

And in context of Healthcare, see Guidelines for reinforcement learning in healthcare, Nature Medicine 2020 by Gottesman et. al. [paper]

3. Causal Inference

The Causal Inference book by Hernan and Robins. The bible for CI in epidemiological context. Freely available here [book]

Introduction to Causal Inference course by Bradley Neal [course]

Concise tutorial by Sharma & Kiciman [tutorial]

TODO - need to find and add the Susan Athey course\tutorial materials. Anyway she has some great stuff you should check out

4. Survival analysis

Concise lecture notes with all the basic math needed to get a hold of this.

Awesome Python package with great explanations of survival analysis in general in the docs: lifelines by Cam Davidson-pilon

Tutorial A Tour of Survival Analysis, from Classical to Modern [videos]

A good recent review [paper]

Once you get a hold on basic survival regression, next step is to understand competing risks and multi-state models. See this tutorial [paper]

And thread with some more recommendations

5. Systems Medicine

Uri Alon’s System medicine course [course]