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May 08, 2024
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CS 457 - Computational Intelligence Description: Introducing concepts, models, algorithms, and tools for development of intelligent systems: artificial neural networks, genetic algorithms, fuzzy systems, swarm intelligence and hybridizations of these techniques. CS 457 and CS 557 are layered courses; students may not receive credit for both.
Prerequisites: Prerequisites: CS 302, CS 325, CS 362 and MATH 330.
Credits: (4)
Learner Outcomes: Upon successful completion of this course, the student will be able to:
- Describe the development and history of computational and artificial intelligence compared to the Turing Machine and conventional computing.
- Discuss the basic concepts of artificial neural networks: learning, classification, pattern recognition, memory, logical operations.
- Build neural classificators and pattern recognition models.
- Describe the use of feedback neural networks for optimization.
- Describe associative memories and, their use in clustering, classification, and visualization of very large data sets.
- Define genetic algorithms and their use in optimization.
- Discuss the basic features of swarm intelligence and ant colony optimization.
- Describe fuzzy logic compared to binary logic. Analyze the use of fuzzy rules for expert systems and the architecture of fuzzy neural systems.
- Describe radial function neural networks.
- Analyze the computational power of neural networks: which are the limits of neural computing.
Learner Outcomes Approval Date: 11/24/10
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