CAP 6545: Machine Learning in Bioinformatics

 
 

Lecture:MW 1:30PM - 2:45PM

Location: BA 0221

Instructor: Dr. Haiyan Nancy Hu

Email: haihu@cs.ucf.edu

Office: HEC- 412

Phone Number: 407-882-0134

Office Hours: MW 2:45PM - 4:15PM

 
 

Description:

In this class, we will provide an overview of the machine learning methods and their applications in Bioinformatics. Also, we will outline some research problems that may motivate the further development of machine learning tools for biological data analysis.

Bioinformatics is an active and interdisciplinary research area. This course is open to all students with background such as computer science, biology, mathematics or statistics who are interested in bioinformatics research.

 
 

Prerequisite:

No formal prerequisite and open to all graduate students.

 
 

Homeworks:

Reading of classic papers

A comprehensive project of applying machine learning methods in Bioinformatics.
 

Grading:

Paper presentation (25%), final project presentation and report (70%), class participation (5%).
 

Primary Schedule:

Date Topic Notes and Readings*
W1: 01/11 Introduction/Administrivia Notes; Readings;
01/13 Bioinformatics overview I Notes;
W2: 01/18 No Class :Martin Luther King Jr. Day
01/20 Bioinformatics overview II Notes;
W3: 01/25 Machine Learning Foundations: the Probabilistic Framework Notes;
01/27 Machine Learning Foundations: the Probabilistic Framework Notes;
W4: 02/01 Probabilistic Modeling and Inference Notes; Readings; Readings;
02/03 Probabilistic Modeling and Inference Notes;
W5: 02/08 Machine learning algorithms overview Notes; (one page final project proposal due)
02/10 Neural Networks Notes; Readings;
W6: 02/15 Neural Networks applications Notes; Readings;Readings; Readings; Readings; Readings; Readings;
02/17 Neural Networks applications Notes; (paper presentation)
W7: 02/22 Hidden Markov Models Notes;
02/24 Hidden Markov Models applications Notes; Readings;Readings;Readings; Readings;
W8: 03/01 Hidden Markov Models applications Notes; Readings;Readings; Readings; (paper presentation)
03/03 Hidden Markov Models applications Notes; (paper presentation)
W9 No Class Spring Break
W10: 03/15 Support Vector Machine Notes;
03/17 Support Vector Machine applications Notes; Readings; Readings;Readings; Readings;
W11: 03/22 Support Vector Machine applications

Notes; Readings; Readings; (paper presentation)
03/24 Support Vector Machine applications Notes; (paper presentation)
W12: 03/29 Bayesian Network Notes; Readings; Readings;
03/31 Bayesian Network applications Notes; Readings; Readings;(paper presentation)
W13: 04/05 Bayesian Network applications Notes; (paper presentation)
04/07 Final project presentation and discussion
W14: 04/12 Final project presentation and discussion
04/14 Final project presentation and discussion
W15: 04/19 Final project presentation and discussion
04/21 Final project presentation and discussion & final project due

* Lecture notes will be put on the website before each class. Reading material is subject to change.