Spring 2009 BMIF330/CS396 Lecture Schedule (Draft 1/27/09)

T/TH 4:00 pm ĘC 5:15 pm (EBL 456)

 

 
 

 

 

 


Date

Lecture Topic

Reading

Note

Instructor

1/09/2009

Opening Session

 

 

SM, HX

1/13/09

Overview of ML

 

DT assignment handed out

AS

1/15/09

Decision trees

 

 

AS

1/20/09

Bayes nets (Definition, properties)

 

BN assignment handed out;

DT assignment due

SM

1/22/09

Bayes nets (Inference)

Lauritzen-Spiegelhalter paper

 

SM

1/27/09

Bayes nets (application paper)

Pathfinder paper

Student presentation

(Daniel)

SM

1/29/09

Neural networks-1

 

NN assignment handed out

BN assignment due

LB

2/3/09

Neural networks-2

 

 

LB

2/5/09

SVM-1

 

SVM assignment handed out

NN assignment due

DH

2/10/09

SVM-2

 

 

DH

2/12/09

HMM-1

BN,HMM,MRF paper

Student presentation

(Vishal)

SVM assignment due

SM

2/17/09

HMM-2

Disease-association-hmm paper

Student presentation

(Ma)

SM

2/19/09

FSS

 

 

AS

2/24/09

Bayes net learning-1

David Heckerman ĘC

A Tutorial on Learning With Bayesian Networks

 

SM

2/26/09

Open

 

Term project assigned

 

3/10/09

Midterm

 

 

 

3/12/09

Bayes net learning-2

 

 

SM

3/17/09

NLP/TM-1

 

NLP/TM assignment handed out

HX

3/19/09

NLP/TM-2

 

 

HX

3/24/09

NLP/TM-3

 

NLP/TM assignment handed due

HX

3/26/09

Bayes net learning-3 (MMHC)

 

Student presentation

SM

3/31/09

Causal discovery (LCD, BLCD)

 

Student presentation

SM 

4/2/09

Causal discovery

(HITON-PC, HITON-MB)

 

Student presentation

SM

4/7/09

Project presentation

 

 

SM, HX

4/9/09

Project presentation

 

 

SM, HX

4/14/09

Project presentation

 

 

SM, HX

4/16/09

Open

 

Take home final exam assigned

 

 

 

 

 

 

 

 

 

 

SM: Subramani Mani

HX: Hua Xu

AS: Alexander Statnikov

DH: Doug Hardin

LB: Laura Brown

 

 

Course Textbooks:

 

1.  Matlab textbook. Hanselman and Littlefield (Recommended)

2.  The Elements of Statistical Learning. Hastie, Tibshirani, Friedman (Recommended)

3.  Christopher D. Manning and Hinrich Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.

 

Software required:

 Matlab student version.  Base version plus the following additional modules:

1.    Statistics

2.    Neural networks

3.    Optimization