UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: EE 443
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
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,
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.
Effective Fall 2012
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 543. 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 543 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 overlap of approximately 15% is
necessary because a small amount of foundational material is required for success in both
classes.
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
Yes
Pass/Fail
No
Both
15. Co-convened with:
EE 543
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.
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?
Effective Fall 2012
Yes
No
Yes
No
18. Prerequisites:
EE 348 & EE 325 with grade C or 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
02/15/2013
Date
Approvals:
2-14-2013
Department Chair/Unit Head (if appropriate)
Date
Chair of college curriculum committee
Date
Effective Fall 2012
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 443 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 Prerequisite:
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 543. 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
Effective Fall 2012
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 Textbook:
Duda and Hart, “Pattern Classification”, 2001, Wiley Interscience, ISBN 0-471-05669-3.
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 paper, and a
comprehensive final exam.
Grading System and Assessment Timing:
Exam 1
100 points
Exam 2
100 points
Final exam
150 points
Homework
100 points
Term Paper
75 points
Participation
25 points
Total
550 points
Effective Fall 2012
approximately week 6
approximately week 11
comprehensive
approximately once per week
includes presentation
attendance and active classroom participation
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
See the following document for these policy statements:
http://www4.nau.edu/avpaa/UCCPolicy/plcystmt.html.
Effective Fall 2012