Date 
Topics 
Readings/References 
Slides/Notes 
Aug 1 
Course Logistics and Introduction 
Nature article, Probability Refresher slides 
slides (print version) 
Foundations and Probabilistic Supervised Learning 
Aug 3 
Basics of Parameter Estimation in Probabilistic Models 
Parameter Estimation (only up to Section 3.1 for now) 
slides (print version) 
Aug 5 (*) 
Probabilistic Models for Regression 
MLAPP Section 7.17.3, 7.5.1, 7.6 (up to 7.6.2) 
slides (print version) 
Aug 17 
Probabilistic Models for Classification (I): Generative Classification 
Optional Readings: PRML Section 4.2, MLAPP Section 4.14.2.5 
slides (print version) 
Aug 19 (*) 
Probabilistic Models for Classification (II): Discriminative Classification 
Optional Readings: PRML Section 4.3, MLAPP Sections 8.18.4, 8.6 
slides (print version) 
Aug 22 
Exponential Family and Generalized Linear Models 
MLAPP Sections 9.19.3, Exponential Family and GLMs 
slides (print version) 
Aug 24 
Hyperparameter Estimation in Probabilistic Models 
PRML Section 3.5 
slides (print version) 
Aug 26 (*) 
Working with Gaussians, Linear Gaussian Models 
MLAPP Sec. 4.1, 4.34.4, PRML Sec. 2.3 
slides (print version) 
Simple Latent Variable Models 
Aug 29 
Introduction to Latent Variable Models, LVMs for Clustering 
MLAPP Sec. 11.111.2.3, PRML Sec. 9.2 
slides (print version) 
Aug 31 
Gaussian Mixture Models (GMM) and Parameter Estimation for GMM 
PRML 9.2  9.3.2, 9.4 
slides (print version), (notes) 
Sept 5 
The Expectation Maximization Algorithm 
PRML 9.39.4, MLAPP Chapter 11, Optional paper reading 
slides (print version) 
Sept 7 
Latent Variable Models for Dimensionality Reduction 
PRML Section 12.2, MLAPP Chapter 12 
slides (print version) 
Approximate Inference 
Sept 12 
Locally (Conditionally) Conjugate Models 

slides (print version) 
Sept 14 
Approximate Inference: Sampling Methods (1) 
PRML Chap. 11 (up to 11.1.4), MLAPP Chap. 23 (up to 23.4.2) 
slides (print version) 
Oct 3 
Approximate Inference: Sampling Methods (2) 
PRML Sec 11.2, 11.3, MLAPP Sec 24.124.3, Recommended: A detailed intro to MCMC, Gibbs Sampling 
slides (print version) 
Oct 5 
Approximate Inference: Sampling Methods (3) 
PRML Sec 11.2, 11.3, MLAPP Sec 24.124.3. Recommended: Gibbs Sampling, MCMC for Bayesian Matrix Factorization 
slides (print version) 
Oct 10 
Approximate Inference: Variational Bayes Inference (1) 
PRML Sec 1010.1, 10.310.4, MLAPP 21.121.3, 21.5. Recommended: Variational Inference Review, Life after EM 
slides (print version) 
Oct 12 
Approximate Inference: Variational Bayes Inference (2) 
Optional but recommended: Section 5.15.2 of this monograph, BBVI paper, SVI paper 
slides (print version) 
Assorted Topics 
Oct 17 
Learning Nonlinear Functions via Gaussian Processes (1) 
MLAPP Sections 15.115.2.5, (Optional: 15.315.5) 
slides (print version) 
Oct 24 
Learning Nonlinear Functions via Gaussian Processes (2) 
MLAPP Sections 15.115.2.5, 15.5 (Optional: 15.315.4), Optional: GPLVM Paper, Deep GP Paper 
slides (print version) 
Oct 26 
Probabilistic Topic Models 
Topic Models Intro 1, Topic Models Intro 2, Optional but recommended: Original LDA Paper 
slides (print version) 
Oct 31 
Deep Probabilistic Models (1) 
Recommended: Chapter 1 and 6 of this tutorial, Optional: Weight Uncertaintly in Neural Networks 
slides (print version) 
Nov 2 
Deep Probabilistic Models (2) 
Recommended: Chapter 6 of this tutorial, VAE paper, Optional: Deep Exp. Family paper, GAN paper 
slides (print version) 
Nov 4 
Nonparametric Bayesian Models for Unsupervised Learning 
Nonparametric Bayesian Models Survey, The IBP paper 
slides (print version) 
Nov 7 
Latent Variable Models for Sequential/TimeSeries Data 
Recommended: (Sections relevant to LDS) from PRML Chapter 13, MLAPP Chapter 18 
slides (print version) 
Nov 9 
Probabilistic Graphical Models, Inference via MessagePassing 
PRML Chapter 8.28.4.4 
slides (print version) 
Nov 14 
Overview of Other Topics, Conclusion and Perspectives 
No readings.. 
slides 