Statistical and Computational Methods for Learning through Graphical Models
 Instructor: Zhenke Wu PhD, Assistant Professor of Biostatistics
 Email: zhenkewu@umich.edu
 Time: Tuesday and Thursday 12:302pm (15 weeks; September 6th to December 13th, 2016)
 Location: 4332 SPH II
 Office Hours: 4623 SPHI (within Suite 4605); Tuesdays 23pm or by appointment
Announcements
 [12/08/2016] Please fill out the [endofterm survey] by December 21, 2016.
 [12/08/2016] [Homework 4] posted; Due to Instructor by 11:59pm on December 21, 2016.
 [11/27/2016] Deadline for extra credit problems: midnight, December 15, 2016.
 [11/08/2016] [Homework 3] posted; Due to Instructor by 11:59pm on December 15, 2016.
 [11/01/2016] [Homework 2] posted; Due to Instructor by 11:59pm on November 21, 2016.
 [10/15/2016] Please fill out the midterm survey here.
 [09/26/2016] Homework 1 due date extended to 11:59pm on October 10th. I have also redistributed the credits to the theory problems and added extra comments. Please refer to the [revised Problem Set 1].
 [09/26/2016] The instructor has moved to a new office 4623 SPHI within Suite 4605.
 [09/19/2016] Problem Set 1 (obsolete; use the revised one) posted. Due 11:59PM, October 3rd, 2016 to Instructorâ€™s email in electronic copy.
 [09/17/2016] Now you can comment to improve the course at the bottom of this page. Or if you like it, please tweet to share for others who are interested in learning, programming and applying graphical models!
 [09/08/2016] Please fill out the class survey for the first week.
Syllabus
The pdf file linked below introduces the course objectives, organizational structures, lectures, references, evaluations and other course policies.
 Syllabus [pdf]
Lecture Notes (required readings at the end of lecture notes):
Calendar of Upcoming Lectures [click to expand]
Module 1 (Representations)
 Lecture 1  Introduction [slides]
 Lecture 2  Dseparation in DAG and Probabilistic Conditional Independence [slides]
 Lecture 3  Dseparation continued (blackboard)
 Lecture 4  Representation for Undirected Graphical Models [slides]
 Lecture 5  DAG and UG: Connections and Differences [slides]
 Lecture 6  Examples of DAG and UG and Conclusion of the Representation Module [slides][RMarkdown file with Shiny Demo]. Please use
RStudio
to run the.Rmd
file to generate ShinyR
Presentation.
Module 2 (Inference and Computation for Graphical Models)
 Lecture 7  Exact inference: factor graphs and variable elimination [slides]
 Lecture 8  Exact inference: Belief Propagation [slides]
 Lecture 9  Exact inference Examples [slides]
 Lecture 10  Junction Tree Algorithm [slides]
 Lecture 12  Examples of Junction Tree Algorithm [marked slides]
 Lecture 13  Approximate Inference by Stochastic Simulation/Sampling Methods [slides]
 Lecture 14  Survey of Automatic Bayesian Software and Why You Should Care [slides][code]
 Lecture 15  Variational Inference Basics [slides][whiteboardnotes]
 Lecture 16  Variational Inference: Examples
Module 3 (Graphical Models for Causality)
 Lecture 18  Causal Inference in Medicine and Public Health: An Introduction [slides]
 Lecture 19/20  Causal Diagram [slides]
 Lecture 21  Marginal Structural Models [Note on IPW]
Module 4 (Case Studies)
 Nov 29: Professor Jian Kang on Graphical Models for Neuroscience.
 Title: Identifying Functional CoActivation Patterns in Neuroimaging Studies Via Poisson Graphical Models
 Abstract: Studying the interactions between different brain regions is essential to achieve a more complete understanding of brain function. In this talk, we focus on identifying functional coactivation patterns and undirected functional networks in neuroimaging studies. We build a functional brain network, using a sparse covariance matrix, with elements representing associations between regionlevel peak activations. We adopt a penalized likelihood approach to impose sparsity on the covariance matrix based on an extended multivariate Poisson model. We obtain penalized maximum likelihood estimates via the expectationmaximization (EM) algorithm and optimize an associated tuning parameter by maximizing the predictive loglikelihood. Permutation tests on the brain coactivation patterns provide region pair and networklevel inference. Simulations suggest that the proposed approach has minimal biases and provides a coverage rate close to 95% of covariance estimations. Conducting a metaanalysis of 162 functional neuroimaging studies on emotions, our model identifies a functional network that consists of connected regions within the basal ganglia, limbic system, and other emotionrelated brain regions. We characterize this network through statistical inference on regionpair connections as well as by graph measures.
 Dec 1: Cancelled Junhyuk Oh on Deep Learning and Reinforcement Learning:
 Title: Improving Generalization via Deep Reinforcement Learning
 Abstract: The ability to generalize from past experience to solve previously unseen tasks or environments is a key research challenge in reinforcement learning (RL). In this talk, I will briefly introduce the basic idea of deep reinforcement learning (Deep RL) and present my recent work that aims to improve generalization ability of RL agents through deep learning. The first work focuses on how to generalize over unseen and larger topologies in 3D world given navigational tasks. The second work discusses how to generalize over new tasks that are described by natural language.

Dec 1: Nested partially latent class models for dependent binary data; estimating disease etiology

Dec 6 and 8: Integrated nested Laplace Approximation with Application to Spatial Statistics [slides]
 Dec 13: Network Basics, Models and Social Network and Infectious Disease Examples [slides]
Homework Assignment
 Problem Set 1[pdf][solution]
 Problem Set 2[pdf] [ps2_data.zip]
 Problem Set 3[pdf]
 Problem Set 4[pdf][solution]