UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: EE 543
2. Units:
See upper and lower division undergraduate course definitions.
3. College:
CEFNS
4. Academic Unit:
3
Electrical Engineering
and Computer Science
5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning
Outcomes)
At the completion of this course, students will understand:
 the probabilistic nature of real signals
 the concept of features and feature space
 how classifiers use discriminant functions to partition feature space
 Bayes rule and the foundations of Bayesian decision theory
 correlation, normalized cross-correlation
 Bayesian classification
 maximum likelihood classification
 K-mean clustering and classification
 linear and simple nonlinear discriminants
 relaxation techniques
 minimum squared error techniques
 the basics of neural networks
 the basics of hidden Markov models
 the basics of machine learning and classification
 how to read and gain understanding from current pattern recognition literature
Students will also develop their abilities to read and gain understanding from current pattern
recognition literature and develop deeper understanding of the above topics in order to help
students in the undergraduate section with their in class exercises, provide them with
constructive feedback on their term papers, and learn how to, and learn how to teach them via
a tutorial and software demonstration.
6. Justification for new course, including how the course contributes to degree program outcomes,
or other university requirements / student learning outcomes. (Resources, Examples & Tools for Developing
Effective Program Student Learning Outcomes).
Pattern Recognition deals with the mathematics, algorithms, and practical implementation
details of detecting important information in the presence of noise and other sources of
confusion. It has wide relevance in such diverse specialty areas as signal processing,
Effective Fall 2012
communications, computer vision, and statistical analysis. Applications include face
recognition, speech recognition, gesture recognition, financial analysis, data mining, and
robotics. Pattern Recognition theory and techniques lie at the heart of these and many other
applications that our students will encounter in their careers.
7. Effective BEGINNING of what term and year?
See effective dates calendar.
Fall 2013
8. Long course title: PATTERN RECOGNITION
(max 100 characters including spaces)
9. Short course title: PATTERN RECOGNITION
(max. 30 characters including spaces)
10. Catalog course description (max. 60 words, excluding requisites):
Survey of techniques for identifying patterns present in noisy signal and image data. Includes
classifiers, discriminant functions, Bayesian decision theory, maximum likelihood, K-means,
relaxation, neural networks, and machine learning. Co convenes with EE 443. Letter grade
only.
11. Will this course be part of any plan (major, minor or certificate) or sub plan (emphasis)?
Yes
If yes, include the appropriate plan proposal.
No
12. Does this course duplicate content of existing courses?
Yes
No
If yes, list the courses with duplicate material. If the duplication is greater than 20%, explain why
NAU should establish this course.
This course does not significantly duplicate material in existing courses, It does co-convene
with the proposed EE 443 Pattern Recognition course. Additionally, it will cover in much
greater depth the basic material of pattern recognition and classification also covered in the
proposed EE 444/544 Computer Vision course. This foundational overlap of approximately
15% is necessary to ensure success in each class.
13. Will this course impact any other academic unit’s enrollment or plan(s)?
If yes, include a letter of response from each impacted academic unit.
14. Grading option:
Letter grade
Pass/Fail
Yes
No
Both
15. Co-convened with:
EE 443
14a. UGC approval date*:
(For example: ESE 450 and ESE 550) See co-convening policy.
*Must be approved by UGC before UCC submission, and both course syllabi must be presented.
16. Cross-listed with:
(For example: ES 450 and DIS 450) See cross listing policy.
Effective Fall 2012
Please submit a single cross-listed syllabus that will be used for all cross-listed courses.
17. May course be repeated for additional units?
16a. If yes, maximum units allowed?
16b. If yes, may course be repeated for additional units in the same term?
Yes
No
Yes
No
EE 325, EE348 with grades of C or
18. Prerequisites:
better.
If prerequisites, include the rationale for the prerequisites.
The prerequisite of EE 348 provides the necessary background in linear systems, discrete-time
signals, and convolution to form a good basis for exploring the pattern recognition topic. EE 348
and EE 325 also provide an important level of mathematical maturity that is vital to understanding
the material in this course. EE 348 requires a background and facility with computer
programming, a skill that is important in this course.
19. Co requisites:
If co requisites, include the rationale for the co requisites.
20. Does this course include combined lecture and lab components?
Yes
If yes, include the units specific to each component in the course description above.
21. Names of the current faculty qualified to teach this course:
No
Dr. Phillip Mlsna, David Scott
Answer 22-23 for UCC/ECCC only:
22. Is this course being proposed for Liberal Studies designation?
If yes, include a Liberal Studies proposal and syllabus with this proposal.
Yes
No
23. Is this course being proposed for Diversity designation?
If yes, include a Diversity proposal and syllabus with this proposal.
Yes
No
FLAGSTAFF MOUNTAIN CAMPUS
Scott Galland
Reviewed by Curriculum Process Associate
03/21/2013
Date
Approvals:
2-14-2013
Department Chair/Unit Head (if appropriate)
Effective Fall 2012
Date
Chair of college curriculum committee
Date
Dean of college
Date
For Committee use only:
UCC/UGC Approval
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
EXTENDED CAMPUSES
Reviewed by Curriculum Process Associate
Date
Approvals:
Academic Unit Head
Date
Division Curriculum Committee (Yuma, Yavapai, or Personal Learning)
Date
Division Administrator in Extended Campuses (Yuma, Yavapai, or Personal
Learning)
Date
Faculty Chair of Extended Campuses Curriculum Committee (Yuma, Yavapai, or
Personal Learning)
Date
Chief Academic Officer; Extended Offices (or Designee)
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
Effective Fall 2012
College of Engineering, Forestry & Natural Sciences
Department of Electrical Engineering & Computer Science
COURSE SYLLABUS: EE 543 PATTERN RECOGNITION
General Information:
Sequence number:
Class times:
3.0 credit hours. There is no laboratory component to this course.
Instructor: Dr. Phillip Mlsna, Associate Professor of Electrical Engineering
Office: Engineering room 257, 523-2112, phillip.mlsna@nau.edu
Office hours as posted (office door and BlackboardLearn)
Official course webpages are on BlackboardLearn: http://bblearn.nau.edu
Course Prerequisites:
EE 348 (Signals and Systems) with grade C or better. EE 325 (Engineering Analysis II) with grade C
or better. You are also expected to have good programming skills in both Matlab and C.
Course Description (from catalog) »
Survey of techniques for identifying patterns present in noisy signal and image data. Includes
classifiers, discriminant functions, Bayesian decision theory, maximum likelihood, K-means,
relaxation, neural networks, and machine learning. Co-convenes with EE 443. Letter grade only.
Student Learning Expectations/Outcomes for this Course :
At the completion of this course, students will understand:
 the probabilistic nature of real signals
 the concept of features and feature space
 how classifiers use discriminant functions to partition feature space
 Bayes rule and the foundations of Bayesian decision theory
 correlation, normalized cross-correlation
 Bayesian classification
 maximum likelihood classification
 K-mean clustering and classification
 linear and simple nonlinear discriminants
 relaxation techniques
 minimum squared error techniques
 the basics of neural networks
 the basics of hidden Markov models
 the basics of machine learning and classification
 how to read and gain understanding from current pattern recognition literature
Students will also develop their abilities to read and gain understanding from current pattern
recognition literature and develop deeper understanding of the above topics in order to help
students in the undergraduate section with their in class exercises, provide them with
Effective Fall 2012
constructive feedback on their term papers, and learn how to, and learn how to teach them via
a tutorial and software demonstration.
Course Structure/Approach:
We will be following the textbook rather closely most of the time, with the topic order as shown in the
“Course Outline” section below. The format will largely be lecture and discussion. The textbook
readings are especially important. There will often be important material in the text that we will not
have time to cover in class.
Required Materials:
Duda and Hart, “Pattern Classification”, 2001, Wiley Interscience, ISBN 0-471-05669-3.
Current journal articles or recent conference papers to be selected by the students and
approved by the instructor for extra homework for those taking the graduate section.
Recommended optional materials/references:
Theodoridis and Koutroumbas, “Pattern Recognition,” 4th ed., 2009, Academic Press, ISBN 978-159749-272-0.
Course Outline
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Week 16
Overview
Chapter 1
Bayesian Decision Theory
Chapter 2
Maximum Likelihood Estimation
Chapter 3
Bayesian Parameter Estimation
Chapter 3
Nonparametric Techniques
Chapter 4
Exam 1
Linear Discriminant Functions
Chapter 5
Multilayer Neural Networks
Chapter 6
Training Neural Networks
Chapter 6
Stochastic Methods
Chapter 7
Nonmetric Methods
Chapter 8
Exam 2
Intro to Machine Learning
Chapter 9
Unsupervised Learning and Clustering
Chapter 10
Hidden Markov Models, project presentations lectures
Project & term paper presentations, review lectures
Final Exam
Assessment of Student Learning Outcomes:
Assessment will be based on two mid-term exams, homework, participation, a term project, and a
comprehensive final exam. Three article reports will require the selection, reading, and
comprehension of materials from the recent research literature in computer vision. Students in EE
543 will also help guide the EE 443 students with in-class exercises and with their EE 443 term
papers. EE 543 students will each review several EE 443 draft term papers and provide constructive
feedback.
Term Project:
For those in the graduate section, a semester project involving a deeper investigation into a relevant
topic and demonstration of software is required. Students will work individually on the term project.
The student will present a background tutorial to the class along with his/her project results and a
Effective Fall 2012
demonstration. In this way, graduate students will provide some educational benefit to the
undergraduates in EE 443.
Grading System and Assessment Timing:
Exam 1
125 points
Exam 2
125 points
Final exam
200 points
Homework
120 points
Term Project
100 points
PR Journal Article Reports 30 points
Participation
25 points
Leadership
25 points
Total
750 points
approximately week 6 (25 points of unique or
additional problems for graduate section)
approximately week 11(25 points of unique or
additional problems for graduate section)
comprehensive (50 points of unique or
additional problems for graduate section)
approximately once per week (20 points of unique or
additional assignments for graduate section)
semester project instead of a term paper (advanced
material and classroom teaching/demonstration
for 25 more points for those in the graduate section)
three written reports for those in the graduate section
attendance and active classroom participation
leadership activities for those in the graduate section
Course Policies:
 Late Work
Assignments are due when specified and can be submitted on BBLearn (preferred)
or on paper at the beginning of the class period. Late work will be accepted
electronically only (on BBLearn, not by e-mailing the professor!) up to 24 hours late for a
20% penalty, and not accepted after 24 hours late.
 Retests and Makeup Tests
No makeup exams will be given except by prior arrangement in exceptional or
emergency situations at the discretion of the instructor. Please contact me immediately
if such a situation arises. (Procrastination is not an emergency.)
 Attendance
Attendance is required and will be recorded on a random basis. Attendance data will be
included in the participation portion of your grade.
 Academic Dishonesty
Cheating and plagiarism are strictly prohibited. Incidents of cheating or plagiarism are
treated quite seriously. The NAU policy on academic dishonesty in Appendix G of the
current Student Handbook applies. All work you submit for grading must be your own.
http://home.nau.edu/studentlife/handbook/appendix_g.asp
You are encouraged to discuss the intellectual aspects of homework assignments with other class
participants. However, each student is responsible for formulating solutions in his or her own words.
University policies:
 Safe Working and Learning Environment
 Students with Disabilities
 Institutional Review Board
 Academic Integrity
 Academic Contact Hour
 Sensitive Course Material
Effective Fall 2012
See the following document for these policy statements:
http://www4.nau.edu/avpaa/UCCPolicy/plcystmt.html.
Effective Fall 2012