### CS688

Pattern Recognition (Fall
2012)

**Meeting Time and Location: **

W
4:30 pm - 7:10 pm,
Robinson Hall B208

**Instructor: **Prof.
Daniel Barbará.

Email: dbarbara (at) gmu (dot) edu

Office:
Eng. Bldg
4420

Office hour: by appointment

**Graduate TA: TBA**

**Course Home Page**

CS688

**Overview**

This course provides an introduction
to the
fundamental concepts in pattern recognition. Topics include:

- Supervised Learning, Linear Models, Statistical Decision Theory
- Linear Methods for Regression
- Linear Methods for Classification
- Kernel Methods
- Model selection and assesment
(other topics as time permits)
**Textbook**

- The Elements of Statistical Learning By Hastie, Tibshirani and Friedman
- A first course in Machine Learning by Rogers and Girolami

**Grading Policies**

There will be written homeworks, programming assignments, a final project a
in-class
midterm exam, and a final exam. Both the final and midterm are open-book
and
open-notes. The final exam will be **comprehensive**, i.e., it will
cover the
entire course. Missed exams must be arranged with the instructor BEFORE
the
exam. Documentation of the illness (doctor's note) is
required. No
early exams will be given and make-up exams are strongly discouraged.

End-of-semester numeric scores will be weighted as follows
(tentative
plan):
- 35% Assignments
- 35% Exams
- 30% project
In order to obtain an A, your
final score
should be
*at least* 90. A total score of 49 or less will result in
an F.
**Late Policy**

Late homework will be
accepted with a
penalty of 20% per day within 3 days after deadlines and will not be
accepted
three days after due, unless under prearranged conditions.
**Honor Code**

You are expected to
abide by
the honor code. All assignments and exams are individual efforts.
Please
refer to GMU Academic
Policies and
Computer
Science Department Honor Code. **Any violation of the honor
code will
result in a zero of the assignment/exam, and may result in an F for the
class.
**

School
Calendar

Disability