Lecture Schedule

Lectures are held on Mondays and Wednesdays from 4:30-5:50 pm in GHC 4307.

All of the lecture videos can be found here.

Date Lecture Scribes Readings Videos
Monday,
Jan 13
Lecture 1 (Eric) - Slides
  • Introduction to Graphical Models
None Required (no reading summary due):
Video
Module 1: Representation
Wednesday,
Jan 15
Lecture 2 (Eric) - Slides
  • Directed Graphical Models: Bayesian Networks
Calvin McCarter,
Daniel Ribeiro Silva (Scribe Notes)
Required:
Video
Wednesday,
Jan 22
Lecture 3 (Eric) - Slides
  • Undirected Graphical Models: Markov Random Fields
Kirstin Early,
Nicole Rafidi
(Scribe Notes)
Required: None
Optional:
Video
Module 2: Exact Inference
Monday,
Jan 27
Lecture 4 (Eric) - Slides
  • Variable Elimination
Pradeep Dasigi,
Soumya Batra,
Manzil Zaheer
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 3
Optional:
  • Koller and Friedman Textbook, Ch. 9
Video
Wednesday,
Jan 29
Lecture 5 (Eric) - Slides
  • Sum-Product Message Passing
Zhe Zhang,
Alex Loewi
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 4
Optional:
  • Koller and Friedman Textbook, Ch. 10
Video
Module 3: Learning
Monday,
Feb 3
Lecture 6 (Eric) - Slides
  • Generalized Linear Models
  • Maximum Likelihood Estimation
  • Sufficient Statistics
Alnur Ali,
Yipei Wang
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 8
Video
Wednesday,
Feb 5
Lecture 7 (Eric) - Slides
  • Learning in Fully Observed Bayesian Networks
Diyi Yang,
Zichao Yang
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 9, Sec. 9.1-9.2
Optional:
  • Koller and Friedman Textbook, Ch. 17
Video
Monday,
Feb 10
Lecture 8 (Eric) - Slides
  • Learning in Fully Observed Markov Networks
Li Zhou,
Meng Song
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 9, Sec. 9.3-9.5
Optional:
Video
Wednesday,
Feb 12
Lecture 9 (Eric) - Slides
  • The Expectation-Maximization Algorithm
Rohan Ramanath,
Rahul Goutam (Scribe Notes)
Required:
  • Jordan Textbook, Ch. 10
Optional:
Video
Module 4: Popular Graphical Models
Monday,
Feb 17
Lecture 10 (Eric) - Slides
  • Modeling Networks
  • Ising Models
  • Gaussian Graphical Models
Zhiding Yu,
Shanghang Zhang (Scribe Notes)
Required:
Optional:
Video
Wednesday,
Feb 19
Lecture 11 (Eric) - Slides
  • Factor Analysis
  • State Space Models
Swabha Swayamdipta,
Dallas Card
(Scribe Notes)
Required:
  • Jordan Textbook, Ch. 14
  • Jordan Textbook, Ch. 15
Optional:
Video
Friday,
Feb 21
Lecture 12 (Eric) - Slides
  • Conditional Random Fields
  • Case Study: Image Segmentation in Computer Vision
Qin Gao,
Siheng Chen
(Scribe Notes)
Required:
Optional:
Video
Module 5: Approximate Inference
Monday,
Feb 24
Lecture 13 (Eric) - Slides
  • Variational Inference: Loopy Belief Propagation
Rajarshi Das,
Zhengzhong Liu,
Dishan Gupta (Scribe Notes)
Required: None
Optional:
Video
Monday,
Mar 3
Lecture 14 (Eric) - Slides
  • Variational Inference: Mean Field Approximation
Amos Ng,
Yu-Hsin Kuo (Scribe Notes)
Required:
Optional:
Video
Wednesday,
Mar 5
Lecture 15 (Eric) - Slides
  • Variational Inference: Theory
  • Case Study: Learning Topic Models
Jingwei Shen,
Jiwei Li (Scribe Notes)
Required:
Optional:
Video
Monday,
Mar 10
No Lecture due to CMU spring break.
Wednesday,
Mar 12
No Lecture due to CMU spring break.
Monday,
Mar 17
Lecture 16 (Eric) - Slides
  • Approximate Inference: Monte Carlo Methods
Calvin Murdock,
Veeru Sadhanala,
Luis Tandalla (Scribe Notes)
Required: Optional: Video
Friday,
Mar 21
Lecture 17 (Eric) - Slides
  • Approximate Inference: Markov Chain Monte Carlo (MCMC)
  • Case Study: Learning Topic Models
Karanhaar Singh,
Dan Schwartz,
Felipe Hernandez (Scribe Notes)
Required: Optional: Video
Monday,
Mar 24
Lecture 18 (Eric) - Slides
  • Advanced Topics in MCMC
Jessica Chemali,
Seungwhan Moon (Scribe Notes)
Required: Optional: Video
Module 6: Nonparametric Bayesian Models
Wednesday,
Mar 26
Lecture 19 (Eric) - Slides
  • Dirichlet Processes
Carl Malings,
Jingkun Gao (Scribe Notes)
Required: Optional: Video
Monday,
Mar 31
Lecture 20 (Eric) - Slides
  • Hierarchical Dirichlet Processes
  • Case Study: Genetic Inference of the World Population
Lavanya Viswanathan,
Manaal Faruqui (Scribe Notes)
Required: Optional: Video
Wednesday,
Apr 2
Lecture 21 (Eric) - Slides
  • The Indian Buffet Process
Jesse Dodge,
Nijith Jacob (Scribe Notes)
Required: Video
Module 7: Spectral Methods for Graphical Models
Monday,
Apr 7
Lecture 22 (Eric) - Slides
  • Hilbert Space Embeddings of Distributions
Sujay Kumar Jauhar,
Zhiguang Huo (Scribe Notes)
Required:
Video
Wednesday,
Apr 9
Lecture 23 (Eric) - Slides
  • Kernel Graphical Models
Xiang Li,
Ran Chen (Scribe Notes)
Required: Optional: Video
Monday,
Apr 14
Lecture 24 (Eric) - Slides
  • Spectral Algorithms for Graphical Models
  • Case Study: Parsing in Natural Languages
Yuan Xie,
Yulong Pei,
Junier Oliva (Scribe Notes)
Required: Optional: Video
Module 8: Structured Sparsity
Wednesday,
Apr 16
Lecture 25 (Eric) - Slides
  • Graph-Induced Structured Input-Output Methods
  • Case Study: Disease Association Analysis
Alok Kothari,
Haoyu Wang (Scribe Notes)
Required: Optional: Video
Monday,
Apr 21
Lecture 26 (Eric) - Slides
  • Structured Sparse Additive Models
Ruikun Luo,
Hao Zhang (Scribe Notes)
Required: Optional: Video
Module 9: Scalable Algorithms for Graphical Models
Wednesday,
Apr 23
Lecture 27 (Qirong, Avi) - Slides
  • Distributed MCMC
Pengtao Xie,
Khoa Luu (Scribe Notes)
Required: Optional: Video
Module 10: Posterior Regularization and Max-Margin Graphical Models
Monday,
Apr 28
Lecture 28 (Eric) - Slides
  • Maximum-Margin Learning of Graphical Models
T. H. Ngan Le,
Felipe Hernandez (Scribe Notes)
Required: Optional: Video
Wednesday,
Apr 30
Lecture 29 (Eric) - Slides
  • Posterior Regularization: An Integrative Paradigm for Learning Graphical Models
Felix Juefei Xu,
Abhishek Chugh (Scribe Notes)
Required: Video
 

© 2009 Eric Xing @ School of Computer Science, Carnegie Mellon University
[validate xhtml]