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The objective of this course is to cover theory and techniques essential for building cyber-physical systems capable of autonomous decision making in the real-world. This course will lay a foundation for theory and techniques in autonomous planning, machine learning, and adaptive sequential decision making. Topics covered include Planning under uncertainty, Bayesian Nonparametric machine learning, Deep learning and Neural Networks, Markov Decision Processes, and Reinforcement Learning. A key emphasis of the course is placed on transition of fundamental aspects of autonomous decision making to application on robotics systems. Course Information: 4 graduate hours. No professional credit. Prerequisite: MATH 225; MATH 416, or equivalent; STAT 400, Math 461 or equivalent. An introductory course in machine learning (e.g. CS 446), control (e.g. SE 422), robotics (e.g. ABE 424, ECE 470), OR Artificial Intelligence (CS 440) is required. An introductory software programming course is recommended