Course outline#

1 Introduction
What is probabilistic programming? How to access the videos, run the notebooks, submit your coding solutions
2 Tensorflow for Machine Learning
Tensorflow, as a matrix based symbolic computing engine for machine learning.
3 Intuitions on Probability
Probability and distributions, marginals, conditionals, likelihood

4 Tensorflow Probability
TF Probability objects, distributions, shapes, layers, etc.
5 Bayesian Modelling
Bayes theorem, uncertainty and knowledge update, priors, likelihood, evidence, posteriors.
6 Variational Inference
Optimization for Bayes modelling, VI on model parameters, VI on data.