ECE 6730 Advanced Neural Networks

Fall 2018
version 20 September 2018


Dr. Damon A. Miller, Associate Professor of Electrical and Computer Engineering, Western Michigan University, College of Engineering and Applied Sciences, Parkview Campus, Room A-240, 269.276.3158, 269.276.3151 (fax),,

Office Hours

Guaranteed office hours are posted on Dr. Miller’s door and at Please respect my office hours.  Other times are available by appointment.

WMU Catalog Description

ECE 6730 Advanced Neural Networks, 3 hrs.  Advanced topics in biological and artificial neural networks from an electrical and computer engineering perspective. Modeling, simulation, and implementation of neural networks. Information theory and knowledge representation. Adaptation and learning. Review of current research.


Prerequisite Abilities


You must be able to work independently on research projects and to write professional quality written reports describing your results. You must also be able to write computer programs using a language such as C, MATLAB®, Mathematica®, etc.


Fall 2018 Semester


Dr. Miller will tailor a course plan for each individual student based on the catalog description and individual research objectives.


Textbook and Materials

1. GC: Practical Convolutional Neural Networks by M. Sewak, Md. Karim, and P. Pujar, Packt Publishing, 2018.

Course Policies

Academic Honesty


“Students are responsible for making themselves aware of and understanding the University policies and procedures that pertain to Academic Honesty. These policies include cheating, fabrication, falsification and forgery, multiple submission, plagiarism, complicity and computer misuse. The academic policies addressing Student Rights and Responsibilities can be found in the Undergraduate Catalog at [] and the Graduate Catalog at []. If there is reason to believe you have been involved in academic dishonesty, you will be referred to the Office of Student Conduct. You will be given the opportunity to review the charge(s) and if you believe you are not responsible, you will have the opportunity for a hearing. You should consult with your instructor if you are uncertain about an issue of academic honesty prior to the submission of an assignment or test.

Students and instructors are responsible for making themselves aware of and abiding by the “Western Michigan University Sexual and Gender-Based Harassment and Violence, Intimate Partner Violence, and Stalking Policy and Procedures” related to prohibited sexual misconduct under Title IX, the Clery Act and the Violence Against Women Act (VAWA) and Campus Safe. Under this policy, responsible employees (including instructors) are required to report claims of sexual misconduct to the Title IX Coordinator or designee (located in the Office of Institutional Equity). Responsible employees are not confidential resources. For a complete list of resources and more information about the policy see

In addition, students are encouraged to access the Code of Conduct, as well as resources and general academic policies on such issues as diversity, religious observance, and student disabilities:

·        Office of Student Conduct

·        Division of Student Affairs

·        University Relations Office

·        Disability Services for Students

— provided by the WMU Faculty Senate Professional Concerns Committee


Plagiarism WILL NOT BE TOLERATED.  For an in-depth exploration of plagiarism, see

Grading Basis

1.      Projects (70%) will be assigned on a regular basis.  Some project results will be reported using the IEEE journal paper format; see for details. You may not use any sources other than those provided in class or in this syllabus when preparing your project report without prior approval from the course instructor.  You may be asked to demonstrate your project. LATE PROJECTS WILL NOT BE ACCEPTED AND ARE DUE AT THE BEGINNING OF CLASS. All projects are to be completed individually.

2.      Homework (30%)

Note: OUTSTANDING WORK might earn extra credit.


Scale: 0-59 E | 60-64 D | 65-69 DC | 70-74 C | 75-79 CB | 80-84 B | 85-89 BA | 90-100 A |
Numeric scores are rounded to the nearest integer.

Grade Appeals:  If you have a question regarding grading of any course materials, see Dr. Miller within FIVE business days of receiving the grade for the assignment in question.  If you disagree with the assessment of that assignment at that meeting, you must submit a written description of your concern to Dr. Miller via his ECE Department mailbox (not email) within five business days of that meeting.


HOMEWORK is assigned in class with a due date. Each problem must be worked on separate page(s).  LATE HOMEWORK will not be accepted, except under extraordinary circumstances. Homework is to be completed individually.


Homework should normally be done on 8 1/2'' by 11'' sheets. “Engineer's Pad” sheets are preferred.  Solutions must be done in a neat, structured, logical, and orderly manner with frequent brief notations enabling the grader to readily verify the author's source of information, steps taken, sources of formula, equations, and methods used. USE THE PARTIAL CHECK LIST FOR SUBMITTED HOMEWORK BELOW.  Papers failing to meet these guidelines may not be graded and may be returned, with or without an opportunity for resubmission with a penalty.




1.      Each problem must include: (a) author's name, (b) name/title of the assignment, and (c) date of completion.

2.      Use only one side of the paper and include a brief and concise statement of the problem prior to its solution. Begin each problem on a new page.

3.      Number the pages and DOUBLE SPACE the text.

4.      Staple each problem in the upper left corner as needed.

5.      Entitle graphs, label and include axes, include key symbols for multiple curve graphs, and give brief notes of explanation where appropriate.

6.      Briefly but clearly annotate your document in a way which will provide the document reader with information such as

a.      which part of the assignment is this?

b.      what is being done and why?

c.      how was it done and what are the results?

d.      how was this equation obtained and how was it used?

e.      sample calculations and definitions of symbols/parameters where appropriate; and



In case of conflict, information in this syllabus supersedes all other course documents.



Students are expected to attend all lectures and to be on time (homework is collected at the beginning of class). Electronic devices are to be turned off (unless there is a safety issue) during lecture unless arrangements have been made with the instructor.


Course Schedule
Will be updated as the semester progresses.


Class #



[some topics may be verbatim from course references]





course introduction

GC: Consider the suitability of Practical Convolutional Neural Networks by M. Sewak, Md. Karim, and P. Pujari as your course text. Provide feedback to Dr. Miller via email by 9/4


LE: Perform a literature search on the topic of applications of multilayer feedforward neural networks to electric utility power load forecasting. The goal is to identify an approach for implementation. Present results in class 9/6.


ZR: Develop a concise verbal/mathematical problem description for your modeling problem. Consider suitability of the text Nonlinear System Identification by O. Nelles as your course text. Present description in class 9/6.








LE: Presentation (12:20PM)
ZR: Presentation (12:30PM)





ZR: Presentation

GC: Use TensorFlow to implement the classifier and approximator project from ECE 5730. Present in class 9/18.


LE: Create training set data for electricity load forecasting. Train neural network. Present in class 9/18.


ZR: Use TensorFlow to implement an example neural network classification problem. Present in class 9/18.








GC Presentation (11:30AM)
LE Presentation
ZR Presentation

GC: Make improvements on use of TensorFlow to implement the classifier and approximator project from ECE 5730. Present in class 9/25.


LE: Expand training set data for electricity load forecasting and train neural network. Present in class 10/2.


ZR: Use TensorFlow to implement an example neural network curve fitting problem. Present in class 9/25








ZR Presentation

LE Presentation









GC Presentation (11:30AM)









Parts adapted/adopted from syllabi by J. Gesink and J. Kelemen.

© 2018 Damon A. Miller

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