CSC 8860 Seminar in Pattern Recognition

Class information

Course #: CSC 8860

Prerequisite: CSC7860 or approval of the instructor.

Day: T-Th

Room: 333 State Hall

Hours: 3.00 p.m. – 4.20.

Instructor information

Instructor: Sorin Draghici

Office: 408 State Hall

Office hours:             Tue: 6.00pm – 7.00pm or by appointment.

Telephone: 577-5484

Email: sod@cs.wayne.edu

Web page: http://www.cs.wayne.edu/~sod

On this web page you can find the syllabus, and announcements regarding the course if any.

Textbooks

No textbook. The seminar will focus on the study of a number of selected research papers to be assigned individually by the instructor to each student.

Course description:

The goal of this course is to present the main research topics in the field of pattern  recognition with applications in bioinformatics. Each student will be assigned a number of research papers to study. A project will be undertaken on an individual basis. At the end of the semester, the students will submit a report describing their work. The grading will be based on this report. Each student will give two presentations during the semester. One presentation will be given in early November and the second presentation will be given at the end of the semester.

Final exam:

No examinations are scheduled for this course.


CSC 7991 Introduction to Data Mining

Class information

Course #: CSC 7991

Prerequisite: CSC 7850  or approval of the instructor. .

Day: T-Th

Room: 0129 State Hall

Hours: 4.30pm- 5.50pm

Instructor information

Instructor: Sorin Draghici

Office: 408 State Hall

Office hours:             Tue: 6.00pm – 7.00pm or by appointment.

Telephone: 577-5484

Email: sod@cs.wayne.edu

Web page: http://www.cs.wayne.edu/~sod

On this web page you can find the syllabus, and announcements regarding the course if any.

Textbooks

Required: Lecture notes

Recommended: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations - Ian H. Witten, Eibe Frank

Course objectives:

The goal of this course is to present the main data mining techniques. The intended audience includes as a central figure the researcher, in particular life scientist, that needs to use computational tools in order to analyze data. At the same time, the course is intended for the computer scientists who would like to use their background in order to solve problems at the border of biology and medicine. The course explains the nature of the specific challenges that such problems pose as well as various adaptations that classical algorithms need to undergo in order to provide good results in this particular field.

Important dates:

Final exam: Friday Dec 13

Midterm exam: Tue, Nov 5

Course contents

Data mining and machine learning; simple examples; input: concepts, instances, attributes; output: decision tables, decision trees, classification rules, association rules; algorithms: divide and conquer, covering algorithms, mining association rules, linear models, instance based learning; Evaluating the learning: training and testing, predicting performance, cross-validation, leave-one-out, bootstrap.




 CSC 6800 - Introduction to Artificial Intelligence




 
 

 CSC 7991 - Data Analysis of Microarray Data