250MLS: Fall 2016: List of Sources
 Textbooks

[B] Pattern Recognition and Machine Learning, by C. M. Bishop,
Springer, 2006.

[M] Machine Learning: A Probabilistic Perspective, by Kevin P. Murphy,
MIT Press, 2012.

[WJ]
Graphical Models, Exponential Families, and Variational Inference,
J. Wainwright and M. I. Jordan, 2008.

[KF]
Probabilistic Graphical Models, D. Koller and N. Friedman, 2009.

[RW]
Gaussian Processes for Machine Learning, Carl Edward Rasmussen and Chris Williams, 2006
 9/7; 9/12

An Introduction to Variational Methods
for Graphical Models, Jordan et al, 1999.

Variational inference Slides
 9/14

MCMC for
Probabilistic Topic Models,
Steyvers & Griffiths, 2007

Latent Dirichlet allocation, D. Blei, A. Ng, and M. Jordan, 2003.

LDA Slides

LDA Notes
 9/19

Dirichlet process review, Y.W. Teh

Variational inference for Dirichlet process mixtures.
D. Blei and M. Jordan.

Dirichlet Process slides
 9/21; 9/26; 9/28 Presentations
 9/28; 10/5
 Graphical models (directed, undirected and
factor graphs) and Belief Propagation
 [AIMA],[B] chapter 8, [KF] chapters 910, [M] chapter 20.

Recursive version of BP for directed case from
Russell and Norvig, 1995 (1st edition).

BP slides part 1

BP slides part 2
 10/3; 10/10; 10/12 no class
 10/17, 10/19
 EP [B]

Minka's Thesis (pages 1326)

EP for GP: [RW] sections 3.6, 3.9

EP slides part 1

EP slides part 2

Not covered but for reference:
Power EP
 10/24, 10/26
 Exponential family distributions and their connection to covex
duality and graphical models: [WJ] Chapter 3.

Exp family slides
 10/31, 11/2
 Variational, BP, EP as "variational optimization": [M] Section
22.3, [WJ] Chapters 4, 5, 7.

Unifying Variational Inference slides

11/7: Planning and inference:
Factorial Hidden Markov Models
Variational methods for Reinforcement Learning

11/9: Generic Methods for Variational Inference:
Stochastic Variational Inference

11/14: Generic Methods for Variational Inference:
Black Box Variational Inference
BlackBox Stochastic Variational Inference
in Five Lines of Python

11/16: Planning and inference:
Variational Algorithms for Marginal MAP
Variational Planning for Graphbased MDPs

11/21: Generic Methods for Variational Inference:
Variational Message Passing

11/28 Overview of Deep networks and
Deep Residual Learning for Image Recognition
Going Deeper with Convolutions
Very Deep Convolutional Networks for LargeScale Image Recognition
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

11/30: Bayesian Models and Deep learning:
AutoEncoding Variational Bayes
Stochastic Backpropagation and Approximate Inference in Deep Generative Models

12/5: Bayesian Models and Deep learning:
Variational Dropout and the Local Reparameterization Trick

12/7: Bayesian Models and Deep learning:
Identity Matters in Deep Learning

12/12: Bayesian Models and Deep learning:
Variational Inference with Normalizing Flows
and its
supplementary material