AI 6600 - Machine Learning and AI

3 Semester Hour(s)

An introduction to the history of machine intelligence; the difference between programming, rules-based systems, and machine learning; disruption; biases in machine intelligence systems; sound approached to model selection; and the challenges of rapid change. Hands-on models to be covered will include classification and predictive (regression) algorithms, optimization, clustering, supervised and unsupervised learning, and simple neutral networks. Includes use of Generative AI models to assess salient journal publications, aid with code changes and error fixes, and for increased learning productivity.