Computer Science Department
University of Western Ontario
CS 434b/654b 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 2:30-4:30 and Wednesday 2:30-3:30
||Middlesex College 320
||Middlesex College 361
||Tuesday 2:00 - 4:00 pm, 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.
Usually I will post those lectures before the class
on the course web site. You may find it helpful to print them out before the class.
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 4 assignments, all weighted equally at 10%
- Assignments will be posted on the course web page.
- Assignments may involve programming in C or/and Matlab.
- Paper copies of assignments are due by midnight on the due date
in the course locker (locker #87 in the basement of the Middlesex College building, next to the grad club,
which is room 19 in the basement)
- 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.
- For the graduate students, there will be extra problems in the assignments. The undergraduate students may
do these extra problems for extra credit (extra credit will be up to 20% of the total weight for undergraduates).
- 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 Jan. 18, due Feb. 1
- Assignment 2: Given out on Feb. 1, due Feb. 15
- Assignment 3: Given out on Feb. 15, due Feb. 24
- Assignment 4: Given out on March 6, due March 20
We will have 4 open book/notes quizzes in this course, I will count the best 3 towards the final grade. I quizzes may be
surprize or may be announced ahead of time.
If you miss more than 1 quizz and have a valid medical or family emergency excuse, please take the supporting documentation
to the office of the Dean of your faculty who will contact me. In this case, I will prorate the remaining quizzes to weight
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 March 8, and the final project is due on April 11. The week of March 20-24
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.
Student collaboration is not allowed for the final project. The projects for the graduate students are expected to
be more extensive than that of the undergraduate students.
- Quizzes 30% (3 best out of 4, 10% each)
- Assignments 40% (10% each)
- Final Project 30%
All assignments are individual assignments. You
may discuss approaches to problems among yourselves;
however, the actual details of the work (assignment coding,
answers to concept questions, etc.) must be an individual
effort. Assignments that are judged to be the result of
academic dishonesty will, for the student's first offence,
be given a mark of zero with an additional penalty equal to
the weight of the assignment also being applied. You are
responsible for reading and respecting the Computer Science
Department's policy on
Scholastic Offences and Rules of
Ethical Conduct .
The University of Western Ontario uses software for plagiarism checking.
Students may be required to submit their written work
and programs in electronic form for plagiarism checking.
For computer-marked multiple-choice tests and/or exams, use
may be made of software to check for unusual coincidences in answer
patterns that may indicate cheating.
Plagiarism: Students must write their essays and assignments in their own
words. Whenever students take an idea, or a passage from another author,
they must acknowledge their debt both by using quotation marks where
appropriate and by proper referencing such as footnotes or citations.
Plagiarism is a major academic offence (see Scholastic Offence Policy in the
Western Academic Calendar).
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.