Date  Lecture  Notes

Readings 
Tuesday,
August 23 
Introduction,
Logistics, Overview 
HW
0 Released HW 0 tex 

Thursday,
August 26 
Probability
distributions, Linear Regression, Sum and Product
Rules, Bayesian Basics (Conjugate Priors,
etc.) 
Lecture
Notes Lecture 2 Supplement 
Optional: Bishop (2006), PRML, Chapters 13 Hinton Lectures on Gradient Descent (6.1, 6.2, 6.3) Cribsheet, Gaussian Identities, Matrix Identities 
Tuesday,
August 30 
Stochastic Gradients, Occam's razor, Support, Inductive Biases, Graphical Models I 
HW 0 Due Lecture Notes Reading Summary (Thesis) Due 
MacKay
(2003): Chapter 28 C. Rasmussen and Z. Ghahramani, Occam's Razor, NIPS 2001 Wilson PhD Thesis, Chapter 1, pages 25, 819. Learning the Dimensionality of PCA, Minka, NIPS 2001 
Thursday,
September 1 
Graphical Models II 
Reading Summary
Ch 8 (up to end of 8.2) due Lecture Notes Written Notes 
Required: Bishop
(2006), Chapter 8 
Tuesday, September 6  Graphical Models III 
Reading Summary Ch. 8 due (complete) 

Thursday, September 8  Graphical Models 

9/13/2016  MCMC 
Reading Summary (Murray) Due. 
Required:

9/15/2016  MCMC, Variational
Methods 
H1 Released 

9/20/2016 
Variational Methods, Laplace
Approximation, Gaussian Processes I 
GP Readings Due 
GPML,
Preface and Chapter 2 Wilson (PhD Thesis), Chapters 1, 2 
9/22/2016  Gaussian Processes II
(Kernel Functions, and Marginal Likelihood Learning) 
GP Readings Due (Ch,
4,5) 
GPML, Chapter 4, 5 
9/27/2016  Kernel Learning 
HW 2 Released GP Readings Due (ICML paper) 
Gaussian
Process Kernels for Pattern Discovery and
Extrapolation, ICML 2013 
9/29/2016  Gaussian Processes III (NonGaussian Likelihoods)  HW1 Due GP Readings Due (Ch 3) 
GPML, Chapter 3 
10/4/2016 
Review Session 

10/6/2016  Midterm 1 

Break  
10/13/2016 
Gaussian Processes IV
(Scalability) 
QuinoneroCandela
& Rasmussen (2005) Wilson et. al (2014) Wilson and Nickisch (2015) 

10/18/2016 
Bayesian Optimization 
HW 2 Due Project Proposal Due 
Snoek
et. al (2012) A review of Bayesian Optimization 
10/20/2016 
Discrete Bayesian Nonparametrics
and the Dirichlet Process Mixture Model 

10/25/2016  Feedforward Neural
Networks 

10/27/2016  Convolutional
Networks 
HW 3 Released 

11/1/2016  Recurrent Networks
and LSTMs 


11/3/2016  Bayesian Neural
Networks 

11/8/2016  Review Session 
HW 3 Due 

11/10/2016  Midterm II 

11/15/2016  Deep Kernel Learning 
Midterm report due 

11/17/2016  Variational
Autoencoder 

11/22/2016  Generative
Adversarial Networks 

Break  
11/29/2016  Project Presentations 

12/1/2016  Project Presentations 

12/7/201612/15/2016  Exams  Project Due 
No final exam for this course 