EECS 435 - Neural Networks

Quarter Offered

Fall : 2-2:50 MWF ; Lin


Engineering Analysis 1 & 4, EECS 230 or EECS 231 (or equivalent), EECS 302.


CATALOG DESCRIPTION: Learning in one-layer and multi-layer feed-forward networks, recurrent networks and dynamical systems. Perceptrons, Hebbian learning, associative memories, Widrow-Hoff learning, backpropagation networks, radial basis function networks, competitive networks, counterprapagation networks, Grossberg network, Adaptive resonance theory, Hopfield networks, simulated annealing, Boltzmann machine


COURSE DIRECTOR: Prof. Wei-Chung Lin

COURSE GOALS: The goal of this course is to provide students with a basic understanding of the fundamentals and applications of artificial neural networks.

PREREQUISITES BY COURSES: Engineering Analysis 1 & 4, EECS 230 or EECS 231 (or equivalent), EECS 302.

PREREQUISISTES BY TOPIC: Linear Algebra, Differential Equations, Probability, Computer programming in MATLAB or C++.


Week 1: Introduction to artificial neural networks, neuron model and network architectures, perceptron learning rule

Week 2: Linear transformations for neural networks, supervised Hebbian learning, optimal linear associative memories

Week 3: Performance surfaces and optimum points, performance optimization, Widrow-Hoff learning

Week 4: Backpropagation learning algorithms, accelerated learning backpropagation algorithms

Week 5: Radial basis function networks, probability networks

Week 6: Associative learning

Week 7: Competitive networks, counterpropagation networks, Grossberg networks

Week 8: Adaptive resonance theory, stability

Week 9: Hopfield networks, bidirectional associative memories

Week 10: Simulated annealing, Boltzmann machine

COMPUTER PROJECTS: Projects on implementation of some neural network models and their applications to real-world problems will be assigned throughout the quarter.


Mid-term exam – 20%

Homework assignment – 50%

Final Exam – 30%

COURSE OBJECTIVES: When a student completes this course, s/he should be able to:

•  Understand the mathematical foundations of neural network models.

•  Design and implement neural network systems to solve real-world problems.