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Full Lecture Notes

Lecture 1 - Introduction [no scribe notes]

Lecture 2 - Discrete Models

Lecture 3 - Multivariate Normal Distributions

Lecture 4 - Linear Regression

Lecture 5 - Linear Classification

Lecture 6 - Exponential Families

Lecture 7 - Neural Networks

Lecture 8 - Backpropagation and Directed Graphical Models

Lecture 9 - Undirected Graphical Models

Lecture 10 - Time Series

Lecture 11 - Exact Inference: Belief Propagation

Lecture 11 - Belief Propagation

Lecture 12 - Recurrent Neural Networks

Lecture 13 - Information Theory

Lecture 14 - Mixture Models

Lecture 15 - Mean Field

Lecture 16 - Variational Inference

Lecture 17 - Loopy Belief Propagation, Gibbs Sampling, and Variational Inference with Gradients

Lecture 18 - Variational autoencoders and GANs

Lecture 19 - Monte Carlo Basics

Lecture 20 - Importance Sampling and Particle Filtering

Lecture 21 - Deep Learning in Health Care