Computer Science Department
University of Western Ontario
CS 434a/541a Pattern Recognition
This is an introductory course to the theory of pattern recognition. Pattern
recognition is concerned with assigning an object (or "pattern") to one of the
several pre-specified categories (or "classes").
This classification is usually performed by finding and utilising useful
features of an object. Recently there has been
an explosion in computing power and digitised multidimensional data
that needs to be analysed. Pattern recognition is a general tool for
analysing multidimensional data. It is used in diverse fields
for tasks such as handwriting recognition, lipreading, geological analysis, medical
data processing, data mining, information retrieval, human-computer interaction, and so on.
In this course we will study basic concepts in the field. We will cover
Bayesian decision theory, maximum likelihood estimation,
nonparametric estimation, linear discriminant functions, support vector
machines, neural networks, unsupervised learning and clustering.
||Monday 11-1 and Wednesday 12-1
||Middlesex College 316
||Middlesex College 361
||1:00 - 3:00 noon, Wednesdays or by appointment
||olga [[at]] csd.uwo.ca
||UWO extension 81417
Students are responsible for ensuring that they have either the prerequisites for this course, or written special permission from their Dean to enrol in.
If a student does not have the course prerequisites, and has not been granted a special permission to take the course by the department, it is his/her best interest to drop the course well before the end of the add/drop period.
If a student is not eligible for a course, he/she may be removed from it at any time, and will receive no adjustment to his/her fees. These decisions can not be appealed. Lack of prerequisites may not be used as the basis of appeal.
- Analysis of algorithms (CS 340a/b)
- First-year course in Calculus
- Introductory Statistics (Stats 222a/b or equivalent)
- Linear Algebra (040a/b)
R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification . John Wiley and sons, second edition. The book will be put on reserve in the library.
- Bayesian decision theory
- Maximum Likelihood estimation
- Non parametric techniques
- Linear Discriminant Functions
- Multilayer Neural Networks
- Unsupervised learning and clustering
The website for the course is
Lecture notes, assignments, and class information will be posted on this website. You are responsible for reading this information frequently.
Some (but not all) of the lectures may be given in Power Point.
I will post those lectures before or shortly after the lecture
on the course web site. Occasionally I will also post lecture notes on the web site.
We will occasionally need to send e-mail messages to the whole class, or to
students individually. E-mail will be sent to your GAUL e-mail address. Make sure
that you read your e-mail on GAUL frequently.
Best Way to Contact me
If you have a question and need to contact me, best way to do so is after the class or during my office hours.
You may to contact me by email,
but do not expect an answer within 2 minutes. I may be able to answer quickly, but it may take me several days.
For questions requiring detailed explanations (like "I didn't get that concept, can you go over it again?"),
I will ask you to come to my office hour. If the scheduled office hours are not convenient, please make
There assignments will be given approximately bi-weekely, and will
involve both theoretical and programming exercises.
- There will be tentatively 5 assignments
- Assignments will be posted on the course web page.
- Assignments may involve programming in C or/and Matlab.
- Paper copies of assignments are due in the beginning of the class.
- You must include the
assignment submission form with your assignment paper copy.
- Assignments should be type-written or written very clearly.
- You may be asked to submit assignments electronically.
- Late assignments: 10% of the mark will be subtracted for each day the assignment is late,
up to the maximum of 5 days. Extensions may be granted only in case of serious medical or family emergency, in which case
you must take supporting documentation to the office of the Dean of your faculty.
- Each assignment may include an extra credit part which may contribute up to 20% toward
the mark on that assignment.
- While students may discuss the assignments, the work is to be done individually by each student.
- Tentative assignment schedule. Note that it is subject to change.
- Assignment 1: Given out on Sep. 20, due Oct. 4
- Assignment 2: Given out on Oct. 4, due Oct. 20
- Assignment 3: Given out on Oct. 20, due Nov. 3
- Assignment 4: Given out on Nov. 3, due Nov. 17
- Assignment 5: Given out on Nov. 17, due Dec. 1
Midterm will be given tentatively on November 8.
I will try to give a sample midterm with solutions a week before the actual midterm.
A makeup for the midterm will be arranged only if it falls on a religious holiday or in case of medical
or family emergency. In any of these cases, please take the supporting documentation to the office of the Dean of
For the final project the students will design and test a pattern classification system.
The students may choose one of several systems proposed by the instructor or may follow their own
idea. The students will also write up a project report, 2 to 5 pages. The report will NOT be
judged for its length. Rather it must include the 3 essential components. First you must state
the problem you will try to solve, then the approach you are going to take to solve it,
and last your results and what you have learned from your results. The results can be negative (that is
my approach did not work so well), if they are negative, you should try to explain possible reasons for
The proposals for the final project
will be due on November 1, and the final project is due on December 8. The week of November 22-28
I will ask you to either write me a one page report on how the final project is going or make an appointment
to see me to discuss how the project is going.
For the final project, student may collaborate in groups of two, in which case the project is expected to be
- Midterm 30%
- Assignments 40%
- Final Project 30%
Each student will have access to an account on the Computer Science Department
senior undergraduate computing facility, GAUL. In accepting the GAUL account,
a student agrees to abide by the department's Rules of Ethical
Note: After-hours access to certain Computer Science lab rooms is by
student card. If a student card is lost, a replacement card will no longer
open these lab rooms, and the student must bring the new card to
the I/O counter. Likewise, if a student card ceases to provide access
where it should, it should be brought the I/O counter as well.
There, the operator will swipe the card,
record the complaint and send the information to the Systems Group who will send notice when they have fixed the problem.