Courses: SYSC 575 / ECE 455/555: Neural Networks I: Winter 2009
Winter 2009, MW, 16:40-18:30, URBN 303
Neural networks is a computational and engineering methodology based on emulating how nature has implemented biological brain (in particular, the brain's massively parallel and learning aspects). As such, it holds promise for significant impact on how important classes of scientific and engineering problems are solved. The objective of the two-term sequence is to have the students obtain a working knowledge of this forefront technology.
This course covers basic ideas of the neural network (NN) methodology, a computing paradigm whose design is based on models taken from neurobiology and on the notion of "learning." A variety of NN architectures and associated computational algorithms for accomplishing learning are studied. Experiments with various of the available architectures are performed via a (commercial) simulation package. Students do a project on the simulator, or do a special programming project.
In addition to the reading assignments, there will be assignments on the simulator to do experimentation with the types of neural network architectures being studied, with a project given during the last 3-4 weeks of the term, based on data and a problem context that I will provide. If a CS student prefers to define and carry out an appropriate programming task, that is negotiable.
Plus, in-class midterm and final exam.
- Neural Networks - A comprehensive foundation, Haykin, Simon, Prentice Hall 1999.
- Neural Computing (tutorial volume of manual for the NeuralWorks simulation package), NeuralWare, Inc., 1993.
Senior standing in EE or CS, or Graduate standing
Note: The $160 "lab fee" charged for this course entitles the student to a CD that contains A) the neural network simulation package NeuralWorks Professional II/Plus (list price $1995), via a site license agreement with NeuralWare, Inc. and B) the User Guide, which includes text #2 above.
- Course Syllabus
- Reading for Problem Assignment #1
- Hints for Assignment #4: BAM
- Neuralware Check Point Bug Fix
- Lendaris Error Surface Handout
- Adaptive Resonance Theory (ART) Paper
- Diffraction-Pattern Sampling for Automatic Pattern Recognition
- Data Preparation for a Neural Network
- Avoiding the Backprop Trap
- Coarse Coding Handout
- Problem Assignment #1
- Problem Assignment 2-4
- Course Project Part A
- Course Project Part B
TA: George (Dave) Lawrence
Office Hours: Thursdays, 1:00 - 4:30 pm
Harder House, Room 207
Also available via e-mail appointment for after class.