CS 3530 - Artificial Neural Networks

Summer 2001

Course Syllabus

PLEASE CHECK THE WEB PAGE OF THIS COURSE (http://www.appstate.edu/~rt/NNt) PERIODICALLY FOR UPDATES, ASSIGNMENTS, CLASS NOTES, ANNOUNCEMENTS, and POSSIBLE CHANGES.

Professor: Dr. Rahman Tashakkori
Office: CAP 127B
Office Phone: 262-7009
Office Hours: MWF 3:00-5:00, and whenever my office door is open.
Class Time/Location: MTWRF 12:40-2:20 pm/Rm 337 CAP.
Lab Location: 439 CAP
Start Date: May 22, 2001    End Date: June 21, 2001
Professor's e-mail:  rt@cs.appstate.edu
Professor's Web Page: http://www.cs.appstate.edu/~rt/



Course Description
We study artificial neural networks from the computer scientist's point of view.   The course provides a thorough introduction to this field. Students will learn to apply the methods and follow the current development in this
field. They implement some of the applications of the Neural Networks in pattern recognition, time series prediction, data mining and optimization problems. We will have several labs in which we will spend sometimes to experiment with a neural network simulators.

Some of the Topics
      the perceptron and linear separable functions
      multi-layer perceptrons
      backpropagation, one basic learning algorithm for feedforward networks
      variations and improvements of backpropagation
      generalization ability
      recurrent networks: Hopfield Networks and Boltzmann Machines
      unsupervised learning
      self-organizing feature maps
      applications

Prerequisite
Computer Science - II (CS 2440) and Linear Algebra

Reference Texts
Neural Networks and Intellect Using Model-Based Concepts
Leonid I. Perlovsky
Publisher: Oxford

Building Neural Networks
David M. Skapura
Publisher: Addison Wesley

Matlab's Neural Networks Toolbox Manual.

Additional readings to be distributed during class.

Grading Policy
The following grading scale will used in this course:
                        Exam (1), 30%,
                        Final exam, 30%,
                        In-class Assignments and Labs 20%,
                        Homework 10%, and
                        Quizzes and class participation %10.

Students are required to attend all classes.  All assignments are due before the start of the class on the due date.  No assignment will be accepted once the solution is discussed in the classroom.   No make-up exams will be given in this course.  If you missed an exam due to an "Extreme circumstances" such as illnesses, death of a relative, or problems of this nature, you have to present documents (e.g. a letter from a doctor, a letter from a hospital, or an obituary from the funeral).  In the such cases, student's final exam's grade will be used for the missed exam.

All assignments and programs MUST be completed by students individually.  You may discuss the assignments and programs among each other but you have to write/edit programs by YOURSELF.   Please see the ASU Academic Integrity Policy for a description of the woes that befall a transgressor!

Grading Scale
A = 90 to 100
B = 80 to 89
C = 70 to 79
D = 60 to 69
F = Below 60

No incomplete grade will be given.

Important Note
If you have a question send me e-mail at any time, and I will try to reply to your e-mail as soon as possible.  I encourage all students to communicate with me using e-mail whenever they have questions.  I will set several online office hours at nights and strongly encourage you to take advantage of those hours.  I also encourage you to check the announcements page periodically for updates regarding our class, assignments, etc....



Final Exam: