Probabilistic Graphical Models
10708, Spring 2016Eric Xing, Matthew Gormley School of Computer Science, Carnegie Mellon University 
Lecture Schedule
Lectures are held on Mondays and Wednesdays from 12:001:20 pm in GHC 4307.Date  Lecture  Scribes  Readings  Anouncements  

Monday, Jan 11 
Lecture 1: Introduction to GM  Slides 
Yuxing Zhang, Tianshu Ren Notes 
Required (no reading summary):

Scribe Template  
Module 1: Representation  
Wednesday, Jan 13 
Lecture 2: Directed GMs: Bayesian Networks  Slides 
Lidan Mu, Lanxiao Xu Notes 
Required (please bring your reading summary):


Monday, Jan 18 
No Lecture due to MLK day.  
Wednesday, Jan 20 
Lecture 3: Representation of Undirected GM  Slides 
Longqi Cai, ManChia Chang Notes 
Required (please bring your reading summary):


Module 2: Classical Methods of Inference & Learning  
Monday, Jan 25 
Lecture 4: Parameter Estimation in Fully Observed BNs  Slides 
Natalie Klein, Purvasha Chakravarti, Dipan Pal Notes 
Required (please bring your reading summary):


Wednesday, Jan 27 
Lecture 5: Learning fully observed directed GM  Slides, Whiteboard 
Yuan Li, Yichong Xu, Silun Wang Notes 
Required (please bring your reading summary):

Homework 1 is out (Jan 30). Due on Feb 15 at 12 noon.  
Monday, Feb 1 
Lecture 6: Learning fully observed undirected GM  Slides, Whiteboard 
Akash Bharadwaj, Sumeet Kumar, Devendra Chaplot Notes 
Required:


Wednesday, Feb 3 
Lecture 7: Exact Inference  Slides 
Keith Maki, Anbang Hu, Jining Qin Notes 
Required:


Monday, Feb 8 
Lecture 8: Learning Partially observed models  Slides 
Cuong Nguyen, Anirudh Vemula, Ankit Laddha Notes 
Required (please bring your reading summary):


Module 3: Popular Graphical Models in Action  
Wednesday, Feb 10 
Lecture 9: Discrete sequential models + CRFs  Slides, Whiteboard 
Pankesh Bamotra, Xuanchong Li Notes 
Required (please bring your reading summary): Optional:


Monday, Feb 15 
Lecture 10: Gaussian graphical models and Ising models: modeling networks  Slides 
Xiongtao Ruan, Kirthevasan Kandasamy Notes 
Required (please bring your reading summary):

Homework 1 due at 12 noon  
Wednesday, Feb 17 
Lecture 11: Factor Analysis and State Space Models  Slides 
Yu Zhang, Syed Zahir Bokhari, Rahul Nallamothu Notes 
Required (please bring your reading summary):

Project proposal due at 12 noon  
Module 4: Approximate Inference  
Monday, Feb 22 
Lecture 12: Variational Inference: Loopy Belief Propagation  Slides 
Jing Chen, Yulan Huang, Yu Fang Chang Notes 
Required (please bring your reading summary):

Homework 2 is out. Due on Mar 16 at noon.  
45:30pm Friday, Feb 26, Porter Hall 125C 
Lecture 13: Mean Field Approximation & Topic Models  Slides 
Shichao Yang, Haoqi Fan, Mengtian Li Notes 
Required (please bring your reading summary):


Monday, Feb 29 
Lecture 14: Theory of VariationalInference: Inner and Outer Approximation  Slides 
Chieh Lo, WeiChiu Ma, Qi Guo Notes 
Required (please bring your reading summary):


Wednesday, Mar 2 
Lecture 15: Approximate Inference: Monte Carlo methods  Slides 
Binxuan Huang, Yotam Hechtlinger, Fuchen Liu Notes 
Required:


Monday, Mar 7 
No Lecture due to CMU spring break.  
Wednesday, Mar 9 
No Lecture due to CMU spring break.  
Monday, Mar 14 
Lecture 16: MCMC  Slides, Whiteboard 
Yining Wang, Renato Negrinho Notes 
Required:


Wednesday, Mar 16 
Lecture 17: Case study with approximate inference  Slides 
Yanyu Liang, ChunLiang Li, Mengxin Li Notes 
Required (please bring your reading summary):

Homework 2 due at 12 noon  
Module 5: Nonparametric Bayesian Models  
Monday, Mar 21 
Lecture 18: Dirichlet Process and Dirichlet Process Mixtures  Slides 
Chiqun Zhang, HsuChieh Hu Notes 
Required:


Wednesday, Mar 23 
Lecture 19: Indian Buffet Process  Slides, Whiteboard 
KaiWen Liang, Han Lu Notes 
Required:

Midway report due at 12 noon  
Monday, Mar 28 
Lecture 20: Gaussian Processes  Slides 
Sai Ganesh Notes 
Required:


Module 6: Spectral Graphical Models  
Wednesday, Mar 30 
Lecture 21: Spectral Learning for Graphical Models  Slides 
Maruan AlShedivat, WeiCheng Chang, Frederick Liu Notes 
Required:

Homework 3 is out (Mar 29). Due on Apr 13 at 12 noon.  
Monday, Apr 4 
Lecture 22: Introduction to Hilbert Space Embeddings and Kernel GM  Slides 
Kevin Lin Notes 
Required:


Module 7: Optimization view of Graphical Models  
Wednesday, Apr 6 
Lecture 23: Graphinduced structured input/output models  Slides 
Raied Aljadaany, Shi Zong, Chenchen Zhu Notes 
Required:


Monday, Apr 11 
Lecture 24: Maxmargin learning of GMs  Slides 
PoWei Wang, Eric Wong, Achal Dave Notes 
Required:


Wednesday, Apr 13 
Lecture 25: Regularized Bayesian learning of GMs  Slides 
TzuMing Kuo Notes 
Required:

Homework 3 due at 12 noon; Homework 4 is to be released.  
Module 8: Deep Learning  
Monday, Apr 18 
Lecture 26: Deep neural networks and GMs  Slides 
Hayden Luse Notes 
Required:


Wednesday, Apr 20 
Lecture 27: Hybrid Graphical Models and Neural Networks  Slides 
Jakob Bauer, Rohan Varma, Otilia Stretcu Notes 
Required:


Module 9: Scalable Approaches for Graphical Models  
Monday, Apr 25 
Lecture 28: Distributed Algorothms for ML  Slides 
Joe Runde, Michael Muehl Notes 
Required:


Wednesday, Apr 27 
Lecture 29: Distributed Systems for ML  Slides 
Petar Stojanov, Christoph Dann Notes 
Required:

Homework 4 due at 12 noon. 
© 2016 Eric Xing @ School of Computer Science, Carnegie Mellon University
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