R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd edition, Wiley-Interscience. ISBN 0-471-05669-3 Errata
Optional Texts (not required):
T. Hastie et al., The Elements of Statistical Learning, Springer-Verlag, 2001.
K. Murphy, Machine Learning: A probabilistic Perspective, MIT Press, 2012.
This course will introduce the fundamentals of pattern recognition.
First, we will focus on generative methods such as those based on Bayes
decision theory and related techniques of parameter estimation and density
estimation. Next, we will focus on discriminative methods such support vector
machines. Methods of pattern
recognition are useful in many applications such as information retrieval, data
mining, document image analysis and recognition, computational linguistics,
forensics, biometrics and bioinformatics. In this course, we will emphasize
computer vision applications.
Course Outline (tentative)
Introduction
Bayesian Decision Theory
Bayesian Networks
Maximum Likelihood Estimation
Dimensionality Reduction
Feature Selection
Bayesian Estimation
Linear Discriminant Functions
Support Vector Machines (SVMs)
Expectation-Maximization (EM) Algorithm
Non-parametric Estimation
Selected Topics
Course Prerequisites
CS 202 with a "C" or better; MATH/STAT 352. Credit hours: 3.0
Exams and Assignments
Grading will be based on several quizzes, two exams, and 4-5 programming assignments. Graduate students will be required to present a paper to the rest of the class. Homework problems will be assigned on a regular basis but will not be collected for grading. Homework solutions will be made available for each assignment.
Course Policies
Lecture slides, assignments, and other useful information will be posted on the 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. 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. Both exams will be closed books, closed notes. If you are unable to attend an 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.
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 15-20 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@cs.unr.edu)