ECSE 6610 Pattern Recognition 

Course Title                                       Pattern Recognition

Course number                                 ECSE 6610

Credit hours                                      3 credits

Semester/ year                                   Spring, 2011

Meeting days                                      12:30-1:50 pm, Tues and Fridays,

Room location                                    JEC 4107

Webpage (if existent)

Prerequisites or other requirements

A good background in linear algebra, probabilities, statistics, optimization, and good programming skills in MATLAB, and/or C++


INSTRUCTOR: Qiang Ji/Professor

Office location: JEC 7004

Office telephone number:  276-6440

Office hours:  Tues and Fridays 2:00pm - 3:00pm pm   or by Appointment

e-mail address:


Teaching Assistant name(s): Li Jia

TA office location: 4-5pm, Mondays

TA office hours:  ECSE Lounge

TA e-mail:


Course description   

This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning. Topics to be covered include linear regression, linear classification, support vector machines, dimensionality reduction, clustering, boosting, and probabilistic graphical models.  


Student Learning Outcomes

·         Student understands the fundamental pattern recognition and machine learning theories

·         Student has  the ability to design and implement certain important pattern recognition techniques

·         Student has the capability of applying the pattern recognition theories to applications of interest.


Required Course text (s)

      C.M. Bishop, "Pattern Recognition and Machine Learning, Springer, 2006


Optional Text

Pattern Classification (2nd Edition),  Richard Duda, Peter Hart, and David Stork,   John Wiley and Sons, 2000.


Course Assessment/measures:

      The course assessments include homework assignments, one middle term exam, class projects, and one final project.   The homework will be done individually by each student.  Class projects can be done individually or collaboratively in a team.  The midterm exam will be open book and it will cover the materials up to the point of the exam.  The final project requires a proposal, a final project report, and a presentation. 


Grading criteria

      Grading will be based on homework assignments, projects, the middle-term  exam, and the final project. There will be 4-5 projects, several assignments,  and a final project. The grade distribution is as follows:


Assignments: 15%
Projects: 50%
Midterm Exam: 20%
Final Project: 15%


Note late homework/project or missed exams will not be accepted without prior approval from the instructor.


Academic integrity

      Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts, which violate this trust, undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities define various forms of Academic Dishonesty and you should make yourself familiar with these. In this class, all assignments that are turned in for a grade must represent the student’s own work. In cases where help was received, or teamwork was allowed, a notation on the assignment should indicate your collaboration. Submission of any assignment that is in violation of this policy will result in a penalty of receiving no credits for the assignment, project, or exam concerned.   If you have any questions, please contact the instructor.  


Lecture Notes