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Syllabus for CIS 525


Fall 2006

Time: Tuesday, 4:40-7:20pm; Place: Tuttleman 305 A


Instructor: Zoran Obradovic

 303 Wachman Hall, zoran@ist.temple.edu, phone: 215 204 6094

Office hours: Tuesday 2-4pm and by appointment

Course materials: www.ist.temple.edu/~zoran/teaching/cis525.htm



Neural networks provide powerful techniques to model and control nonlinear and complex systems. The course is designed to provide an introduction to this interdisciplinary topic. The course is structured such that students from computer science, engineering, physics, mathematics, statistics, cognitive sciences and elsewhere have an opportunity to explore promising research topics by a hands-on experience with neural network simulators applied to classi_cation and prediction problems ranging from bio-medical sciences to finance and business.



Stat503 or CIS511 and undergraduate understanding of probability, statistics and linear algebra.



Haykin S. Neural Networks: A Comprehensive Foundation (2nd Edition), Prentice Hall, 1999, ISBN 0-13-273350-1 (required).

Bishop, C.M. Neural Networks for Pattern Recognition, Oxford University Press, 1996, ISBN 0-19-853864-2(optional).


Topics:will be tailored to interests of the participants. Content will include:

I. Supervised and Unsupervised Neural Networks

1. Multilayer Perceptrons

2. Radial-Basis Function Networks

3. Committee Machines

4. Principal Components Analysis

II. Selected Advanced Topics

1. Self-Organizing Maps

2. Information Theoretic Models

3. Temporal Processing

4. Dynamically Driven Recurrent Networks

5. Applications

III. Reading and research projects presentations.


Grading: Homework (30%), midterm exam (20%), reading/presenting assignments (20%) and an individual research project (30%).


Late Policy and Academic Honesty: No late submissions will be accepted. Discussing materials with fellow students is acceptable, but programs, experiments and the reports must be done individually.