Sep 21, 2019  
2017-2018 Undergraduate Academic Catalog 
    
2017-2018 Undergraduate Academic Catalog [ARCHIVED CATALOG]

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EE 484 - Neural Networks

3 lecture hours 0 lab hours 3 credits
Course Description
This course introduces students to the basic concepts of modeling and simulating adaptive and learning systems using neural networks. The underlying concepts of neural networks are introduced, as well as a number of common topologies and learning rules used in neural networks. Students gain firsthand experience of neural networks through computer assignments and a short research project. (prereq: CS 2510  or equivalent, MA 383 )
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
  • Describe the basic configurations of neural networks
  • Describe and implement  simple neural networks
  • Formulate engineering problems for which neural networks may be a suitable solution
  • Evaluate the suitability of neural network architectures and learning algorithms for engineering problems
  • Use commercially available neural network development tools
  • Interpret and critique scholarly articles in the area of neural networks

Prerequisites by Topic
  • High-level language programming with objects or structures
  • Calculus (gradients, series expansions)
  • Matrix arithmetic

Course Topics
  • Introduction to neural networks, problems, terminology, MATLAB toolbox (2 classes)
  • Data gathering and formatting (2 classes)
  • Linear perceptron and multilayer backpropagation networks(4 classes)
  • Training algorithms and associated mathematics (4 classes)
  • Radial Basis Networks (3 classes)
  • Self-Organizing Maps (1 class)
  • Time Series Networks, Control Sytems, and Adaptive Filtering (4 classes)
  • Special Topics (5 classes)
  • Project workshops (5 classes)

Coordinator
Sheila Ross



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