Henry S. Baird Spring 2015 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.

This is 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 for sale in the Univ. bookstore, consider other sources:

- Amazon.com: (a) Hardcopy new, $128 (as fast as two-day delivery); (b) Hardcopy used, $66 and up (2-7 day delivery); (c) Kindle edition, $121 (autodelivered wirelessly).
- Buy or borrow from a former CSE/Math 441 student: ask the instructor for a list of students to ask.

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 this book is **not** required.

"Desk copies" of these two textbooks may be consulted in
the
CSE Dept office PL 354 when the office is open (but the copy cannot be
taken away). Other
extra copies are available in Dr. Baird's office (PL 380) when he's
there (but, again, these copies can't be taken away).

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

Instructor:
**Prof.
Henry Baird**, CSE Dept., 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):**

To
ask him 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 "Midterm" exam (in class),
probably on Thursday April 9; 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 a 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-SP15 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 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 212 (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.