This course explores modern approaches in machine learning and soft computing. Course topics include a brief introduction to knowledge-based intelligent systems, rule-based expert systems and a rule-based programming language (CLIPS or Jess), methods of handling uncertainty (Bayesian methods and fuzzy logic), artificial neural networks (including self-organizing maps), evolutionary computation, and hybrid intelligent systems. Students would also have the opportunity to work on emerging topics or technologies including deep learning. Prerequisites: CMST 191 - Introduction to Public Speaking, ◆ CS 341 - Data Structures, and either MATH 212 - Calculus I or MATH 140 - Applied Calculus. Grade only. Offered annually (usually fall semester).