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<HTML><HEAD><TITLE> EE539 Home Page </TITLE></HEAD><BODY BGCOLOR=FFFFEE TEXT=980D13 LINK=2211EF VLINK=8A02AC ALINK=8A02AC><FONT SIZE= 7><CENTER>EE539: Neural Networks & Applications<BR><!WA0><IMG WIDTH=555 SRC="http://www.ee.upenn.edu/images/hr_frac4.gif"><FONT SIZE= 6>Nabil H. Farhat</CENTER><P><FONT SIZE= 3><H2>Table of Contents</H2><H2><!WA1><A HREF="#description"> Brief Description</A><BR><!WA2><A HREF="#general"> General Information</A><BR><!WA3><A HREF="#syllabus">Course Syllabus</A><BR><!WA4><A HREF="#textbook">Textbook</A><BR><!WA5><A HREF="#grading">Grading Policy</A><BR><!WA6><A HREF="#exams">Exam Dates</A><BR></H2><BR><BR><H2><A NAME="description">Course Description</A></H2>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, formal 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.<H2><A NAME="general">General Information</A></H2><UL><H3>Nabil H. Farhat</H3>Nabil H. Farhat<BR>Room 372 Moore<BR>Phone: 898-5882<BR><!WA7><A HREF="MAILTO:farhat@ee.upenn.edu">Email:farhat@ee.upenn.edu</A><BR><P><H3>Office Hours</H3>If unavailable, please see, Drucilla Spanner, Room 363 Moore, 898-6823<H3>Prerequisite</H3>None (Undergraduates need permission of Instructor)<H3>Time and Location</H3>TTh, 3-4:30, 223 Moore</UL><H2><A NAME="syllabus">Course Syllabus</A></H2><UL><LI>Topic 1, Review of Essential Properties of the Biological Neuron and the Nervous System<LI>Topic 2, Essentials of Nonlinear Dynamical System Theory<LI>Topic 3, The Hopfield Model and Spin Glasses<LI>Topic 4, Stochastic Neural Networks and the Boltzmann Machine<LI>Topic 5, Multilayer Feedforward Networks for Supervised Learning<LI>Topic 6, Unsupervised and Competitive Learning Algorithms<LI>Topic 7, Bifurcating Neural Networks</UL><H2><A NAME="textbook">Textbooks</A></H2><UL><H3>Main texts</H3><OL><LI><I>Neural Network Architectures </I>, Dayhoff, J., Van Nostrand Reinhold, 1990.</OL><H3>Reference</H3><OL><LI><I>Neural Computing: Theory and Practice, </I>Wasserman, Philip, D., Van Nostrand Reinhold, 1989<LI><I>Introduction to the Theory of Neural Computation </I>Hertz, J., Krogh,A., and Palmer, R.G., Addison Wesley, 1991<LI>Supplementary classnotes and material for additional reading will be handed out in class.</OL></OL></UL><H2><A NAME="grading">Grading Policy</A></H2><UL><LI>Homeworks: 1/3<LI>Midterm: 1/3<LI>Final: 1/3</UL><H2><A NAME="exams">Exam Dates</A></H2><UL><LI>Midterm 1: TBA<LI>Final: Mo.Dec. 18, 1:30-3:30 pm</UL><HR Size=3><!WA8><A HREF="mailto:farhat@ee.upenn.edu">Nabil H. Farhat</A><BR>Updated: Sept. 21, 1995</BODY></HTML>⌨️ 快捷键说明
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