Week

Day

Date

Topic

Reading

Assigned

Due

Points

1

Tue

9-20-2011

Intro and Review of Probability

Chapter 2

Hmwk1

9-28-11

15

1

Thu

9-22-2011

Statistical Estimation

Chapter 17.1

 

 

 

2

Tue

9-27-2011

Bayes Networks

Chapter 3

 

 

 

2

Thu

9-29-2011

Bayes Networks and Naive Bayes Classifiers

 

Hmwk2

10-10-11

11

3

Tue

10-4-2011

No class

 

 

 

 

3

Thu

10-6-2011

Markov Networks, Variable Elimination

Chapters 4, 9

 

 

 

4

Tue

10-11-2011

Junction Tree

Chapter 10

 

 

 

4

Thu

10-13-2011

Sampling

Chapter 12

Hmwk 3

10-24-11

10

5

Tue

10-18-2011

Learning

 

 

 

 

5

Thu

10-20-2011

Learning (cont.), Structure Learning

Chapter 18

 

 

 

6

Tue

10-25-2011

Expectation-Maximization

Chapter 19

Hmwk 4

10-31-11

14

6

Thu

10-27-2011

Semi-supervised Learning

 

 

 

 

7

Tue

11-1-2011

HMMs (cont.), Presentation Guidelines, and exam review

 

 

 

 

7

Thu

11-3-2011

Mid-term Exam

 

 

 

 

8

Tue

11-8-2011

Large Scale Text Classification using Semi-supervised Multinomial Naive Bayes

Presenter: Downey

 

 

 

 

8

Thu

11-10-2011

Active Learning by Labeling Features

Presenters: Chen Jin, Xu Chen, Chandra Sekhar

 

 

 

 

9

Tue

11-15-2011

Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and Instances

Presenters: Mark Cartwright and Yi Yang

 

 

 

 

9

Thu

11-17-2011

CoBayes: Bayesian Knowledge Corroboration with Assessors of Unknown Areas of Expertise

Presenters: Sam Carton and Michael Wurtz

 

 

 

 

10

Tue

11-22-2011

Active Learning from Crowds

Presenters: Yu Cheng, Xie Yusheng, Diana Palsetia

 

 

 

 

10

Thu

11-24-2011

No class

 

 

 

 

11

Tue

11-29-2011

Reducing Label Cost by Combining Feature Labels and Crowdsourcing

Presenters: Alex Ansari and Alex Madjar

 

 

 

 

11

Thu

12-1-2011

Soylent: A Word Processor with a Crowd Inside

Presenters: Shawn O'Banion, Kathy Lee