Computer Science & Engineering Department

• Email: bebis@unr.edu
• Office: 411 WPEB
• Office Hours: Monday/Wednesday 3:45pm - 5:00pm and by appointment

Required Text:

R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition, Wiley-Interscience. ISBN 0-471-05669-3 Errata

Optional Texts:

T. Hastie et al., The Elements of Statistical Learning, Springer-Verlag, 2001.
K. Murphy, Machine Learning: A probabilistic Perspective, MIT Press, 2012.
K. Murphy, Probabilistic Machine Learning: An Introduction, MIT Press, 2022.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI)
Pattern Recognition (PR)
Machine Learning (ML)
International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI)

IEEE Computer Vision and Pattern Recognition (CVPR)
International Conference of Pattern Recognition (ICPR)

Math Cheat Sheet (lots of useful formulas)
Numerical Recipes in C
Applied Statistics

Important Resources

UCI Machine Learning Repository
Review Papers on Statistical Pattern Recognition, Neural Networks and Learning

Useful software:

Statistical Pattern Recognition Toolbox
Tooldiag
Feature Selection Toolbox (C++)
VLFeat open source library
R Project

Source Code for Reading/Writing Images

Course Prerequisites

Exams and Assignments

Grading will be based on 7 quizzes (lowest quiz grade will be dropped), two exams, and 4 programming assignments (to be completed on an individual basis). Graduate students will be required to present a paper to the rest of the class.

Course Policies

Lecture slides, assignments, and other useful information will be posted on this web page. Discussion of the of your work is allowed and encouraged. However, each student should do his/her own work. Assignments which are too similar will receive a zero and you might also receive an "F" in the class. No late work will be accepted unless there is an extreme emergency. If you are unable to hand in an assignment by the deadline, you must discuss it with me before the deadline. Quizzes and exams will be closed books, closed notes. If you are unable to take a quiz or exam you must inform me in advance. No incomplete grades (INC) will be given in this course and a missed exam may be made up only if it was missed due to an extreme emergency. Regular attendance is highly recommended. If you miss a class, you are responsible for all material covered or assigned in class. You should carefully read the section on Academic Dishonesty found in the UNR Student Handbook. Your continued enrollment in this course implies that you have read it, and that you subscribe to the principles stated therein.

Handouts

• Syllabus

Sample Exams

Lectures

• Course Overview
  • What is Pattern Recognition? A Gentle Introduction
    
  • Jain, A.K.; Duin, P.W.; Jianchang Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 22, no 1, pp. 4-37, Jan. 2000.
  
  
• Introduction to Pattern Recognition
  
  
• Review of Linear Algebra (see Appendix A.2)
  • Matrix Algebra (StatLect)
  • Linear Algebra Review and Reference
  • Linear Algebra (Khan Academy)
  • Linear Algebra (online book - UC-Davis)
  
  
• Review of Probability (see Appendices A.4 and A.5)
  • Probability and Statistics Refresher (from Univ of Florida)
  • Probabilities and Statistics refresher (from Stanford Univ)
  • Statistics and Probability (Khan Academy)
  • Probability and Statistics (online book - Univ of Toronto)
  • Introduction to Probability (online book - Harvard and Stanford Universities)
    
  • Multivariate normal distribution

Programming Assignments

Paper Presentation (Graduate Students Only)

Presentation Guidelines

1. Presentations should be professional as if it was presented in a formal conference (i.e., powerpoint slides/projector).
2. Your goal is to educate and inform your audience. Make sure your presentation follows a logical sequence. Help the audience understand how successive definitions and results are related to each other and to the big picture.
3. You should have your remarks prepared and somewhat memorized. Reading from your notes excessively should be avoided.
4. Anticipate Questions: think of some likely questions and plan out your answer. Understand the Question: paraphrase it if necessary; repeat it if needed. Do Not Digress. Be Honest: if you can't answer the question, say so.
5. Each student's material is different but 15 minutes each should be enough time for your presentation.
6. Meet the eyes of your audience from time to time.
7. Vary the tone of your voice and be careful to speak clearly and not talk too quickly.