Fairfield
University School of Engineering
Electrical
and Computer Engineering
COURSE: SW461 Pattern
Recognition  spring, 2017
Instructor:
Jeffrey N. Denenberg 
Office:
Bannow 301C 
Google
Voice: (203) 5139427 
Office
Phone: x3330 
Email: jeffrey.denenberg@ieee.org 
INSTRUCTOR
ASSISTANCE: TBD, and by phone or email.
CLASS
HOURS: Tuesdays,
6:30 – 9:00 PM, in Bannow TBD
This
programming intensive course introduces the student to the algorithms and data
structures used in modern pattern recognition systems with an emphasis on those
that can learn and improve their performance as they are used. After a short review of some necessary
mathematical concepts (Probability, Stochastic Processes and Vector Spaces),
the student is introduced to the problem of representing realworld problems to
a system. Students will, in teams,
design, build, test and report on a major subsystem of a pattern recognition
system. Selected real world applications
are used to show examples of some valid representations (e.g. Speech and
Handwriting) to provide insight and experience in the application of
recognition systems. Several important
recognition engines are then studied and analyzed for their effectiveness as
the basis for recognition, synthesis, and learning systems. The use of additional knowledge bases dealing
with the problem environment is then introduced to increase system performance.
Prerequisites: Basic math and algebra. Some calculus may be helpful.
Some
background in probability and statistics.
Programming
skills (MatLab, “C” or Java)
Textbook: “Pattern
Recognition”,
Sergios Theodoridis and Konstantinos Koutroumbas,
Academic
Press, Ed. 4, 2009, ISBN 9781597492720.
References: https://www.coursera.org/learn/machinelearning/,
http://en.wikipedia.org/wiki/Machine_learning,
www.sci.utah.edu/~gerig/CS6640F2010/probtut.pdf
http://en.wikipedia.org/wiki/Variableorder_Markov_model
deeplearningreinventsthehearingaid
Video Lectures Pattern
Recognition: Prof.
P. S. Sastry, Indian Institute of Science, Bangalore
Probability Primer: MathematicalMonk on
youtube
Markov Models: MathematicalMonk on youtube
Grading
Exams
(2) 
40% 
Exercises/Participation 
20% 
Project 

Report 
10% 
Presentation 
10% 
Implementation 
20% 
Disability
If you have a documented disability and wish to
discuss academic accommodations, please contact the Office of Academic Disability
Support Services at (203) 2544000, x2615, and notify the course instructor
within the first two weeks of the semester.
Distance
Education Students
The course is set up to support a small
number distance education students. Lecture
notes and supplementary videos are accessible via links in this syllabus and
via Blackboard. You wiil submit scanned
copies of any quizzes or exams via email.
Schedule:
Date 
Topic 
Notes 
Videos 
HW 
1/17 
Course Introduction, 
Pattern Recognition Basics, Noise Introduction, Linear Algebra, PatternRecognitionTeamProjects 
Introduction 
Get ahead in the readings 

Domain Representation: 



1/24 
Handwriting: A
quantized, 2D vector space in time1 
Microsoft2005 – (HMM) 

Ch2: Computer Experiments
Be ready to display and describe your solutions. 
1/31 
Speech: A
MultiDimensional vector space 
Intro to
ASR+HMMs 

Ch3: Computer
Experiments Be ready to display and describe your solutions. 
2/7 
Data Clustering 
clustering algorithms1, clustering algorithms2, clustering algorithms3, clustering algorithms4 

Ch4: Computer
Experiments 
2/14 
Exam 1 – Domain
Representation 



2/21 

No Class – Tuesday Follows Monday Schedule 


2/28 
Exam 1 Reprise, 


Ch5: Computer
Experiments 

Recognition Engines 



3/7 
Dynamic Time Warping
(Dynamic Programming) 

Ch6: Computer
Experiments 

3/14 
Spring Recess – No Class 




Markov Models 



3/21 
N^{th}Order
Markov Models 
Smith et. al. (March 1985) 

Ch7: Computer
Experiments 7.1, 7.2 
3/28 
HMM 



4/4 
Neural Networks 




Exam 2 – Recognition Engines 


Ch8: Computer
Experiments 
4/11 
Interim Recognition
Project Reports 


Ch9: Computer
Experiments 

Team Project Presentations 



4/18 
Representation 1 



4/25 
Representation 2 



5/2 
Recognition Engine 1 



5/9 
Recognition Engine 2 
5/45/11 Final Exam Week 


Learning
Outcomes
No. 
Outcome 

1 
The student will understand the problems encountered,
approaches to and how to design a pattern recognition system. 
Knowledge, Application, & Synthesis 
a, c, e,
f, h, j, k 
2 
The
student will understand how to test a recognition system and quantify the
results. 
Analysis

a, e, k 
3 
The
student will be able to use mathematical analysis to reducing the complexity
of real world pattern recognition applications. 
Application 
a, c, e,
k 
4 
The
student will be able to design and implement a major component of a pattern
recognition system. 
Synthesis 
a, c, e,
k 
Teacher
Responsibilities
Distribute syllabus.
Review the material described in the syllabus.
Explain material.
Identify alternate reading assignments or books
that clarify the material.
Relate material to "real world"
situations when possible.
Answer questions.
Be available to discuss problems.
Be receptive to new ideas.
Announce business/class conflicts in advance.
Make up missed classes, but classes are never cancelled as they will be done online.
Prepare and administer exams.
Grade fairly.
Assign appropriate home problems.
Homework policy – reviewed in class, Quizzes
cover the HW topics
Student
Responsibilities
Be familiar
with the prerequisite material as well as the Computer Tools and Tutorials:
Ask questions and stay current.
Study the material described in the syllabus.
Preferably before it covered in class and do some of the problems with answers
in the back of each assigned chapter.
Complete the assigned homework.
Obtain class notes and homework if a class is
missed. View Author’s lecture video on that
week’s topic(s)
Use the library and the Internet to obtain
supplemental material.
Prepare for quizzes/exams.
Ask for help from me (I have office hours) and/or
your fellow students.
Note: All exams in this course are open book,
but not open computer (or phone) so relying on an eBook or PDF will put you at
a disadvantage.