Nov 28, 2023
CS 557 - Computational Intelligence and Machine Learning
Introducing intelligent systems: artificial neural networks, deep learning, evolutionary computation, fuzzy systems, swarm intelligence, and hybridizations of the above techniques. We will look at these techniques from a machine learning perspective. CS 457 and CS 557 are layered courses; students may not receive credit for both. Course will not have an established scheduling pattern (Winter).
Prerequisite: CS 528.
Upon successful completion of this course, the student will be able to:
- Compare the development and history of computational and artificial intelligence as compared to the Turing Machine and conventional computing.
- Describe the basic concepts of artificial neural networks: learning, classification, pattern recognition, memory, logical operations.
- Build neural classifiers and pattern recognition models.
- Describe the use of feedback in 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.
- Compare fuzzy logic to binary logic and 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 and determine the limits of neural computing.
- Lead group discussions pertaining to building neural classifiers and pattern recognition models. [graduate level]
- Prepare presentation for demonstrating basic concepts of learning, classification, pattern recognition, memory, and logical operations. [graduate level]
- Research and present information pertaining to associative memories and their use in clustering, classification, and visualization of very large data sets. [graduate level]
Learner Outcomes Approval Date:
Anticipated Course Offering Terms and Locations:
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