One of my computational learning goals for 2019 is probablistic machine learning.
This repository contains my notes and code on probabilistic programming using Python, especially the PyMC3 library.
My primary learning resources for theory are:- Machine Learning- A Probabilistic Perspective by Kevin Murphy (e-book,effective so far),
- Course Notes for Bayesian Models for Machine Learning by John Paisley (Columbia University, google it)
- Information Theory, Inference and Learning Algorithms by David Mackay (ebook, more intense deeper read but worthwhile so far)