Fairfield
University School of Engineering
Electrical
and Computer Engineering
COURSE: SW461 Pattern
Recognition - Spring, 2019
|
Instructor:
Jeffrey N. Denenberg |
Office:
Bannow 301C |
|
Google
Voice: (203)
513-9427 |
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 Nursing 203
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 real-world 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.
Pre-requisites: Basic math and algebra. Some calculus may be helpful.
Some
background in probability and statistics.
Programming
skills (MatLab, Python, C/C++, and/or Java)
Textbook: “Pattern
Recognition”,
Sergios Theodoridis and
Konstantinos Koutroumbas,
Academic
Press, Ed. 4, 2009, ISBN 978-1597492720.
References: https://www.coursera.org/learn/machine-learning/,
http://en.wikipedia.org/wiki/Machine_learning,
www.sci.utah.edu/~gerig/CS6640-F2010/prob-tut.pdf
http://en.wikipedia.org/wiki/Variable-order_Markov_model
deep-learning-reinvents-the-hearing-aid, Predictive Analytics
for Dummies
Video Lectures Pattern
Recognition: Prof.
P. S. Sastry, Indian Institute of Science, Bangalore
Probability Primer: MathematicalMonk
on youtube
Markov Models: MathematicalMonk
on youtube
Linear-algebral: Engineer4Free.com
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) 254-4000, 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 of distance education students. Lecture
notes and supplementary videos are accessible via links in this syllabus and
via Blackboard. You will submit scanned
copies of any quizzes, exercises or exams via email.
Schedule:
|
Date |
Topic |
Notes |
References |
HW |
|
1/22 |
Course Introduction, |
Pattern Recognition Basics, Noise Introduction, Linear Algebra, PatternRecognitionTeamProjects, |
Get ahead in the readings |
|
|
|
Domain Representation: |
|
|
|
|
1/29 |
Handwriting: A
quantized, 2-D vector space in time1 |
Microsoft-2005 – (HMM) |
|
Ch2: Read Be ready to display
and describe your solutions. |
|
2/5 |
Speech: A Multi-Dimensional
vector space |
Intro to
ASR+HMMs |
|
Ch3: Computer
Experiments Be ready to display and describe your solutions. |
|
2/12 |
Data Clustering |
clustering algorithms-1, clustering algorithms-2, clustering algorithms-3, clustering algorithms-4 |
|
Ch4: Computer
Experiments |
|
2/19 |
Tuesday is Monday |
No class – Monday classes are held on Tuesday |
|
|
|
2/26 |
Exam 1 – Domain
Representation |
|
|
|
|
3/5 |
Exam 1 Reprise, |
|
|
Ch5: Computer
Experiments |
|
|
Recognition Engines |
|
|
|
|
3/12 |
Dynamic Time Warping
(Dynamic Programming) |
|
Ch6: Computer
Experiments |
|
|
3/19 |
Spring Recess – No Class |
|
|
|
|
|
Markov Models |
|
|
|
|
3/26 |
Nth-Order
Markov Models |
Smith et. al. (March 1985) |
|
Ch7: Computer
Experiments 7.1, 7.2 |
|
4/2 |
HMM |
|
Ch8: Computer
Experiments |
|
|
4/9 |
Neural Networks |
deep-learning-reinvents-the-hearing-aid, neuralnetworks, neural, neuralnetworksanddeeplearning |
|
|
|
4/16 |
Exam 2 – Recognition Engines |
|
|
Ch9: Computer
Experiments |
|
|
Team Project Presentations |
|
|
|
|
4/23 |
Exam2 Reprise |
|
|
|
|
4/30 |
Project 2, Project 3 |
|
|
|
|
5/7 |
Project 4, Project 5 |
5/3-5/10 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 on-line.
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.