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Henry S. Baird    Spring 2014 Course

Pattern Recognition

CSE 426/326   3 credits

CRNs:  18017 (426); 18016 (326)

Note to undergraduates:  you are very welcome in this course; if you take it at the 326 level, you'll do the same programming exercises as the grad students, but shorter HWs and exams.

Note to CS M.S. graduate students:  this course fulfills both the "Theory" and "Applied Theory" skill areas.

An introduction to the state of the art of pattern recognition, and the machine-learning theory, algorithms, and systems architectures that underlie them.

Theoretical topics will include Bayesian decision theory, statistically trainable vector-space classifiers, parametric classifiers (for, e.g.,likelihoods with Gaussian densities), non-parametric classifiers (e.g. nearest neighbors), Perceptrons, generalized linear discriminants, kernel-based methods, decision trees, support-vector machines, neural nets, ensembles, and randomized classifiers. Also, we study general methodological issues, including best practices for statistical training and testing, the curse of dimensionality, and feature selection.

The last 1/4 of the course focuses on engineering challenges illustrated in applications chosen from the computer-vision R&D literature. These reflect state-of-the-art approaches to segmentation, contextual analysis (including syntax and semantics), autonomous adaptation, style-conscious recognition, and anytime algorithms.

Weekly written homeworks or short programming exercises.  A midterm exam.  Students will have a choice among (1) a final exam, (2) a software project on a research problem from computer vision, docunment image analysis, digital libraries, or Web security, or (3) a literature review report on a narrow technical topic related to this course.

Course objective:   On completing this course, students will be sufficiently familiar with the theory, notation, and vocabulary of pattern recognition and machine learning to be able to pursue matters of interest in the current technical literature. They will also have a grasp of key engineering issues arising in applications.

Textbook: Pattern Classification (2nd Ed.), R. O. Duda, P. E. Hart, & D. G. Stork, John Wiley & Sons, October 2000. 680 pages. ISBN 0-471-05669-3.  If there are no copies available in the Univ. bookstore, email me immediately. (There is also available, from the same publisher, a companion Computer Manual in MATLAB for this textbook; if you want to use MATLAB for the programming exercises in this course, I urge you buy this manual also---but it is not required.)

Lectures Time and Place: Tues/Thurs 9:20 AM - 10:35 AM, Packard Laboratory room 258.

Instructor:   Prof. Henry Baird, hsb2@lehigh.edu. Office:  Packard Lab 380.   Office Hours:  Wednesdays 12:10-1:00 PM or by appointment. He will grade all exams and final project reports.

Grader (for homeworks only):

Our grader is:

    Zachary Daniels   zad309@lehigh.edu

To ask questions about homework problems or grades, contact him by email; if you wish, he will arrange to meet you in person.  He is also available to explain ideas in this course.

Exams:   There will be one 1:20 hour exam (in class), on Thursday March 25, and one three-hour final exam (during end-of-semester exam period):  these are all closed-book, written exams.

Students may choose not to take the Final Exam; instead, they may carry out either (a) a software project on a research problem from computer vision, docunment image analysis, digital libraries, or Web security, or (b) a literature review report on a narrow technical topic related to this course.

(Exams are never repeated: if under extraordinary circumstances a student cannot take an exam, it will be assigned a grade which is the average of the other two exams' grades.  If you anticipate that you must---for excellent reasons---miss any of these exams, please give me as much notice in advance as you can, and I will try to arrange for you to take the exam (or some roughly equivalent version of it) before the scheduled exam date.)

Course Site:  CSE-426-CSE-326-SP14 Pattern Recognition.  We will use the University's online CourseSite facility to distribute lecture slides, homework assignments, experimental data sets, and grades.  As soon as you are enrolled in this course, please browse coursesite.lehigh.edu  and try to login:  if you cannot, send the instructor email.

Prerequisites (if you have any questions about these, ask the Instructor):

CSE/Math 340:  Design and Analysis of Algorithms -- or comparable background in basic algorithms and data structures.

Math 205: Linear Algebra etc -- or similar familiarity with linear algebra & matrices.

Math 231 or Math 309 or CSC 450: Applied Probability & Statistics -- or some background in discrete probability and applied statistics & data analysis.

CSE 109: Programming in C++ -- or enough experience with C++, Java, C, or (ideally) MatLab to complete a small software project without faculty supervision.

If you have any questions, ask the instructor: hsb2@lehigh.edu.

Accommodations for Students with Disabilities:  If you have a disability for which you are or may be requesting accommodations, please contact both your instructor and the Office of Academic Support Services, University Center C212 (610-758-4152) as early as possible in the semester.  You must have documentation from the Academic Support Services office before accommodations can be granted.

Lehigh University endorses The Principles of Our Equitable Community (http://www4.lehigh.edu/diversity/principles). We expect each member of this class to acknowledge and practice these Principles. Respect for each other and for differing viewpoints is a vital component of the learning environment inside and outside the classroom.


© 2003 P.C. Rossin College of Engineering & Applied Science
Computer Science & Engineering, Packard Laboratory, Lehigh University, Bethlehem PA 18015