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Table of Contents
Brief Description
General Information
Course Syllabus
Textbook
Grading Policy
Exam Dates
Course Description
Examines the application of paradigms in neural networks to problems in pattern classification, optimization, function approximation, and machine learning. The course will include: review of the physiology and anatomy of neurons and neuron networks, form
al models of neurons and networks; attractor networks, associative memory; storage capacity; the pattern classification problem; neural classifiers; optimization by energy minimization, solving the TSP (Traveling Salesman problem) with attractor networks;
simulated annealing and the Boltzmann machine; hardware implementations of neural networks; the problem of learning; algorithmic approaches; perceptron learning; back-propagation; randomized algorithms; and genetic algorithms.
General Information
Nabil H. Farhat
Nabil H. Farhat
Room 372 Moore
Phone: 898-5882
Email:farhat@ee.upenn.eduOffice Hours
If unavailable, please see, Drucilla Spanner, Room 363 Moore, 898-6823
Prerequisite
None (Undergraduates need permission of Instructor)
Time and Location
TTh, 3-4:30, 223 Moore
Course Syllabus
Textbooks
Main texts
Reference
Grading Policy
Exam Dates
Nabil H. Farhat
Updated: Sept. 21, 1995