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CMPE 452 Neural Networks and Genetic Algorithms Units: 3.00
Artificial Neural Networks (ANN) and Genetic Algorithms (GA) for problem solving and prediction tasks such as classification, clustering, optimization and data reduction and modeling human cognition, with application to real world problems. Ongoing research in this area in various application domains.
(Lec: 3, Lab: 0, Tut: 0)
(Lec: 3, Lab: 0, Tut: 0)
Requirements: Prerequisites: ELEC 278 or MREN 178 or permission of the instructor
Corequisites:
Exclusions: ELEC 425
Offering Term: F
CEAB Units:
Mathematics 9
Natural Sciences 15
Complementary Studies 0
Engineering Science 12
Engineering Design 0
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Explain the concepts behind the operation of biological neurons and the evolution of artifical neural network (ANN) to model connections as a representation of information.
- Design and implement different types of ANNs using a variety of design techniques and learning algorithms for prediction, clustering, classification, storage, and function approximation
- Apply optimization techniques such as simulated annealing and genetic algorithms with ANN training algorithms
- Describe with reference to recent research work how ANNs are used to simulate human cognition, vison, and memory, and are applied to intelligent systmes for language and image processing, decision support systems, and predictive systems.
- Explain the power and limitations of neural network systems.