kfupm

King Fahd University of Petroleum & Minerals

College of Computer Sciences and Engineering

Information and Computer Science Department

PR

 

ICS 583: Pattern Recognition (3-0-3) [Graduate]

 

Syllabus – Spring Semester 2012-2013 (122) [PDF]

 

 

 

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Website:

All course material and resources are posted in Blackboard (WebCT) http://webcourses.kfupm.edu.sa

 

Class Time, Venue and Instructor Information:

 

 

Time

Venue

Instructor

Office Hours

UT

6:30-7:45pm

24/146

Dr. EL-SAYED EL-ALFY

Office: 22-108

Phone: 03-860-1930

E-mail: alfy@kfupm.edu.sa,

http:faculty.kfupm.edu.sa/ics/alfy

 

 

Announced on Blackboard

 

Course Catalog Description

 

Various methods of pattern recognition, extraction methods, statistical classification, minmax procedures, maximum likelihood decisions, data structures for pattern recognition, case studies.

Pre-requisites: Consent of the Instructor

 

 

The course is designed to be self-sufficient. However, some previous experience with linear algebra, probabilities, programming in MATLAB or any other programming language is desirable.

Course Objectives

 

Provide students with the fundamentals of pattern recognition to solve real-world problems.

Expose students to hands-on experience and research skills to effectively apply pattern recognition techniques to real-world problems.

 

Course Learning Outcomes

 

Upon completion of the course, you should be able to:

  1. Recognize the nature and inherent difficulties of the pattern recognition problems.
  2. Explain concepts, principles, trade-offs, and techniques of major topics in pattern recognition.
  3. Select suitable pattern recognition technique and effectively apply it to solve real-world problems.
  4. Design and implement pattern recognition algorithms using a programming language (e.g. MATLAB, C/C++/C#, and Java).
  5. Properly interpret and communicate the results clearly and concisely using pattern recognition terminology.

 

 

 

Required Material

Pattern Classification, 2nd Edition (0471056693) cover image

     R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2/E. Wiley, 2001.

 

 

    Lecture Handouts   (via Blackboard)

 

 

 

 

 

Other Recommended References

 

    C. M. Bishop. Pattern Recognition and Machine Learning, Springer, 2006.     

  S. Theodoridis, K. Koutroumbas and K. Koutroumbas. Pattern Recognition, 4/E, Academic Press, 2008.

  S. Theodoridis, A. Pikrakis, K. Koutroumbas and D. Cavouras. Introduction to Pattern Recognition: A Matlab Approach, Academic Press, 2010.       

  Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press 2012.        

  For some topics, research papers, tutorials, etc. will be used.  (via Blackboard)

Assessment Plan

 

 

 

 

Assessment Tool

Weight

Class work:

Homework assignments

Quizzes

Presentations

Active participation in Blackboard discussions/blogs

25%

8

10

4

3

Term Project

Project proposal

Survey and review of related work

Project report + Presentation + Prototype demo

35%

3

7

25

Midterm     (8th Week, Tues.)

15%

Final Exam

25%


 

 

Tentative Topics

 

 

#

Topics

Ref. selected topics

Additional Activities

1

Introduction to pattern recognition

Ch. 1

 

2

Review of probability theory and linear algebra

Notes, App. A

 

3

Statistical pattern recognition and Bayesian decision theory

Ch.2

 

4

Maximum likelihood and Bayesian parameter estimation

Ch. 3

 

5

Nonparametric techniques

Ch. 4

 

6

Decision trees

Ch. 8

 

7

Linear discriminant functions

Ch. 5

 

8

Machine learning: Neural networks

Ch. 6

 

9

Classifier combination

Ch. 9

 

10

Feature selection

Notes

 

11

Unsupervised machine learning: Clustering and K-means

Ch. 10

 

12

Other techniques and case studies (as time permits)

Notes

 
 

 

 

How to do well and become a star?

 

skill

 

 

 

Sample AI Applications

 

 

App

      

            

 

 

 

 

Other Resources on the Web:

[Sorted Collection is posted in Blackboard]