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Mar 13, 2025
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EE 484 - Neural Networks3 lecture hours 0 lab hours 3 credits
Course Description
This course introduces students to the basic concepts of modeling 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, including deep learning. Students gain firsthand experience in the creation, training, and application of neural networks through computer assignments and a short research project. (prereq: (EE 2510 or CS 1011 or BE 2200 ) and 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 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 systems, and adaptive filtering (4 classes)
- Deep learning (3 classes)
- Special topics (2 classes)
- Project workshops (5 classes)
Coordinator
Dr. Sheila Ross
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