| Lecture |
Date |
Topics |
Readings |
Paper
presentations |
| 1 |
May 1 |
Course overview (Lecture slides) | [KF] Chapters 1, 2 |
|
| 2 |
May 3 |
Directed graphical models: Bayesian Networks (Lecture slides) | [KF] Chapter 3 |
|
| 3 |
May 8 |
Inference, variable elimination (Lecture slides) |
[KF] Chapter 9 |
|
| 4 |
May 10 |
Intro to Machine Learning (Lectures slides) |
[KF] Chapter 16 |
|
| 5 |
May 15 |
Intro to Machine Learning (no
slides) |
[KF] Chapter 16 |
|
| 6 |
May 17 |
Parameter estimation (Lecture slides) |
[KF] Chapter 17 |
1)
Exact
Linkage
Computation
for
General
Pedigrees M. Fishelson and D. Geiger Bioinformatics, 18(1), 189-198, 2002 Presenters: Sungjoon Cho |
| 7 |
May 24 |
Undirected graphical models:
Markov networks and Conditional random fields (Lecture slides) |
[KF] Chapter 4 |
|
| 8 |
May 29 |
Structured Potentials (no new
lecture slides) |
[KF] Chapter 5 |
2) Fuchun Peng and Andrew
McCallum
(2004). Accurate
Information
Extraction
from
Research
Papers
using
Conditional
Random
Fields. In Proceedings of Human Language Technology Conference
and North American Chapter of the Association for Computational
Linguistics (HLT/NAACL-04), 2004. Presenters: Gaurav Baruah, Kanwaljit Singh, Filip Krynicki |
| 9 |
May 31 |
Weighted model counting (Lecture slides) |
||
| 10 |
Jun 5 |
Weighted model counting |
3)
Exploiting
Causal
Independence
Using
Weighted
Model
Counting Wei Li, Pascal Poupart and Peter van Beek In Proceedings of the 23rd National Conference on Artificial Intelligence (AAAI), Chicago, Illinois, 2008. Presenters: Tzu-Yang Yu, John Morcos, Xu Chu |
|
| 11 |
Jun 7 |
Inference as optimization (Lecture slides) |
[KF] Chapter 11 |
|
| 12 |
Jun12 |
Inference as optimization (Lecture slides) |
[KF] Chapter 11 |
4)
Expectation-Propagation
for
the
Generative
Aspect
Model Thomas Minka, John Lafferty UAI, 352-359, 2002 Presenters: Claude Richard, Elnaz Barshan Tashnizi, Sahil Singla |
| 13 |
Jun 14 |
Sampling techniques (Lecture slides) |
[KF] Chapter 12 |
|
| 14 |
Jun 19 |
Sampling techniques |
[KF] Chapter 13 |
5)
CONDENSATION--Conditional
Density
Propagation
for
Visual
Tracking Isard and Blake, International Journal of Computer Vision, 1998 Presenters: Andrew Codd, Josip Pavic, Celine Craye |
| 15 |
Jun 21 |
MAP inference (Lecture slides) |
[KF] Chapter 13 |
|
| 16 |
Jun 26 |
MAP inference (Lecture slides) |
[KF] Chapter 13 |
6)
A
Comparative Study of Energy Minimization Methods for Markov Random
Fields with Smoothness-Based Priors Szeliski, R. ; Zabih, R. ; Scharstein, D. ; Veksler, O. ; Kolmogorov, V. ; Agarwala, A. ; Tappen, M. ; Rother, C. IEEE Transactions on Pattern Analysis and Machine Learning, 30(6), 1068-1080, 2008 Presenters: Qu Chen, Stacey Jeffery |
| 17 |
Jun 28 |
Expectation Maximization (Lecture slides) |
[KF] Chapter 19 |
|
| 18 |
Jul 3 |
Bayesian parameter Estimation (Lecture slides) |
[KF] Chapter 17 |
7)
Comparative
Analysis
of
Probabilistic
Models
for
Activity
Recognition
with
an
Instrumented
Walker Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung and Allan Caine Uncertainty in Artificial Intelligence (UAI), Catalina, CA, 2010 Presenters: Mohammad Rahman, Nazanin Mohammadi, Jinqiu Yang |
| 19 |
Jul 5 |
Parameter Estimation for
Undirected Models (Lecture slides) |
[KF] Chapter 20 |
|
| 20 |
Jul 10 |
Neural networks (Lectures slides) |
Bishop, Pattern Recognition and
Machine Learning, Sections 5.2, 5.3 |
8)
A
real-time
expectation-maximization
algorithm
for
acquiring
multiplanar
maps
of
indoor
environments
with
mobile
robots Sebastian Thurn, Christian Martin, Yufeng Liu, Dirk Hahnel, Rosemary Emery-Montemerlo, Deepayan Chakrabarti, Wolfram Burgard IEEE Transactions on Robotics and Automation, 20(3), 433-442, 2004 Presenters: Quan Zhou, Noha Adel Elprince, Ravi Chandra |
| 21 |
Jul 12 |
Probabilistic graphical models
as neural networks (Lecture slides) |
||
| 22 |
Jul 17 |
Deep Learning (Lecture slides) |
9)
Acoustic
Modeling
Using
Deep
Belief
Networks A. Mohamed, G.E. Dahl, G. Hinton IEEE Transactions on Audio, Speech and Language Processing, 20(1), 14-22, 2012 Presenters: Ross Hacquebard, Andrew Cameron, David Kaufman |
|
| 23 |
Jul 19 |
Markov Logic Networks (Lecture slides) |
||
| 24 |
Jul 24 |
Lifted inference (Lecture slides) |
10)
Efficient
Lifting
for
Online
Probabilistic
Inference, Aniruddh Nath, Pedro Domingos. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010. Presenters: Xiang Ji, Shawn Eastwood, Amer Abdo |