Expectation-Maximization (EM) Algorithm (if time permits)
Course Prerequisites
CS 202 with a "C" or better; MATH/STAT 352 or MATH/STAT 461. Credit hours: 3.0
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
Sample Exams
Lectures
- Course Overview
- Introduction to Pattern Recognition
- Review of Linear Algebra (see Appendix A.2)
- Review of Probability (see Appendices A.4 and A.5)
- Bayesian Decision Theory
- Case Studies
- Maximum Likelihood (ML) Estimation
- Bayesian Estimation
- Midterm Exam Review
- Dimensionality Reduction (with case studies)
- M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
- Daniel L. Swets, John (Juyang) Weng Using Discriminant Eigenfeatures for Image Retrieval , IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 18, No 8 , pp. 831-836, 1996.
- A. Martinez and A. Kak PCA versus LDA, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 23, No 2, pp. 228 -233, 2001.
- C. Burges, Dimension Reduction: A Guided Tour, Microsoft Research Tech Report MSR-TR-2009-2013.
- Feature Selection (with case studies)
- Feature Selection Algorithms - A Brief Guide
- 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.
- H. Liu et al., Feature Selection: An Ever Evolving Frontier in Data Mining, Fourth Workshop on Feature Selection in Data Mining, 2010.
- Z. Sun, G. Bebis, X. Yuan, and S. Louis, Genetic Feature Subset Selection for Gender Classification: A Comparison Study, IEEE Workshop on Applications of Computer Vision, pp. 165-170, Orlando, December 2002.
- Z. Sun, G. Bebis, and R. Miller, Object Detection Using Feature Subset Selection, Pattern Recognition, vol. 37, pp. 2165-2176, 2004.
- G. Bebis, A. Gyaourova, S. Singh, and I. Pavlidis, Face Recognition by Fusing Thermal Infrared and Visible Imagery, Image and Vision Computing, vol. 24, no. 7, pp. 727-742, 2006.
- Linear Discriminant Functions
- Support Vector Machines
- C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Kluwer Academic Publishers, 1998.
- A. Mojan, C. Papageorgiou and T. Poggio, Example-based object detection in images by components, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 23, no. 4, pp. 349-361, 2001.
- B. Moghaddam and M. Yang, Gender Classification with SVM, IEEE Transactions on PAMI, vol. 24, no. 5, 2002.
- M. Pontil and A. Verri, Support Vector Machines for 3D Object Recogition, IEEE Transactions on PAMI, vol. 20, no. 6, 1998.
- Bayesian Networks
- Final Exam Review
Programming Assignments
Submission Instructions
Assignment 1 (due on 3/11/2024 at 11:59 pm)
Assignment 2 (due on 4/1/2024 at 11:59pm)
Assignment 3 (due on: 4/22/2024 at 11:59pm)
- ppt file
- Face images (please note that the header of the PGM images provided has all the values in the same line, e.g., P5 48 60 255)
- Eigen-decomposition example
- jacobi.c and jacobi.h (from Numerical Recipes in C)
- Phillips, J. and Moon, H. and Risvi, S. and Rauss, J., The FERET evaluation methodology for face recognition algorithms, vol. 22, no. 10, pp. 1090-1104, 2000.
- The Face Recognition Homepage
Assignment 4 (due on: 5/7/2024)
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.
Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557
Page created and maintained by:
Dr. George Bebis
(bebis@cse.unr.edu)