Introduction
Probability
Variational Inference
MCMC
Bayesian ML
Graphical Models
Mixture Models
Information Theory
Variational Models
Bayesian ML with PyMC
Bayesian ML with Pyro
Bayesian ML with PyTorch
Bayesian ML with Tensorflow Probability
Bayesian ML with Julia
Gaussian Processes
Linear Regression
Generative Adversarial Networks
Hidden Markov Models
Tensor Factorization
Neural Networks
Expectation Maximization
Recommender Systems
Active Learning
NNs for Time Series
JAX
Appendix 1 - Linear Algebra for ML
Appendix 2 - Stochastic processes
References
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