NEURAL NETWORKS

ECEN 5733

Spring 2017

 Instructor  Course Schedule  Grading

 Text, Software and Notes
  Disability Statement

 Homework Assignments

Instructor

Instructor: Dr. Martin Hagan
Phone: (405) 744-7340
Office: 311 ES
email: mhagan at okstate.edu
Office Hrs: 3:30-5:30 MWF (Other times available by appointment.)

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Text, Software and Notes

Text:

Neural Network Design (2nd Ed) - Hagan, Demuth, Beale, O. De Jesús

Software:

MATLAB® will be used for some homework assignments and the project.
It is available in college laboratories, or obtain the student version for
use at home. Tutorials for MATLAB can be found here.

Neural network design demonstrations.

Notes:

Course notes available on the Web (Powerpoint or PDF format).

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Tentative Course Schedule

 Week  Topic  Chapter
 1  Introduction  1-4
 2  Linear Algebra Background  5-6
 3  Coincidence Learning (Hebb Rule)  7
 4  Conditions for Optimality  8
 5  Optimization  9
 6  Performance Learning (Widrow-Hoff)  10
 7  Exam #1 (March 10)
 8  Backpropagation  11
 9  Extensions of Backpropagation  12
 10  Generalization  13
 11  Dynamic Networks  14
 12  Competitive Learning (Kohonen)  15-16
 13  Feature Maps (Kohonen)  16
 14  Exam #2 (April 28)  
 15  Radial Basis Networks  17

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Grading and Examination Policy

2 Exams - 25 pts each
Quizzes/Homework - 25 pts
1 Project - 25 pts
1 Comprehensive Final Exam - 25 pts (Friday, May 12, 2pm)

The top three scores from the three exams and the total quiz/homework score will be added to the project score to obtain the total grade for the course (out of a total of 100 pts). All exams and quizzes will be closed-book/closed-notes (1 sheet of 8.5x11 notes allowed). No make-up exams unless previous arrangements have been made. Students will be expected to attend class and prepare assignments. Habitual failure to do so will result in a reduced grade. An incomplete grade will only be given when a student misses a portion of the semester because of illness or accident. Cheating on examinations, plagiarism and other forms of academic dishonesty are serious offenses and may subject the student to penalties ranging from failing grades to dismissal.

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Disability Impairment Statement

If any member of the class feels that he/she has a disability and needs special accommodations of any nature whatsoever, the instructor will work with you and the University Office of Disabled Student Services to provide reasonable accommodations to ensure that you have a fair opportunity to perform in this class. Please advise the instructor of such disability and the desired accommodations at some point before, during, or immediately after the first scheduled class period.

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