CSC 471 - Artificial Intelligence/Neural Nets

The student will be introduced to concepts used in modern Artificial Intelligence and Neural Networks systems that make it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. The four types of AI will be explored; Reactive machines, Limited memory machines, Theory of mind, and Self-awareness. A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. Different neural network architectures (Unsupervised Pretrained Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Recursive Neural Networks) and their learning methods (supervised, unsupervised, and reinforcement) will be examined.

3 Semester Credit(s)

Laboratory/Experience Hours: N/A
Prerequisite(s): ENR 304 & MAT 306
Corequisite(s): N/A
Repeatable for Credit: No
Core Course: No
Grade Type: LT
Typically Offered: Fall