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Graduate level introductory course on machine learning and reinforcement learning. Artificial intelligence (AI) is rapidly growing in virtually all science and engineering fields. Technologies related to machine learning are at the center of this trend. This course provides a fundamental and core knowledge on machine learning and reinforcement learning, which in turn prepares students so as to self-advance into the state-of-the-art AI technologies in a variety of fields. This course will discuss general aspects of machine and reinforcement learning, which is suitable for students in different fields of interest, though the primary applications include robotics engineering. Topics that will be covered include: core mathematics necessary, core principles for supervised and unsupervised learning (e.g., linear regression, logistic regression, Bayes nets, EM, and so on), and for reinforcement learning (e.g., Markov decision process, dynamic programming, etc.). Homework assignments include both theoretical and computational components. No Audits Recommended Course Background: o Course background: Linear Algebra, Multivariate Calculus, Probability, Differential Equations; o Programming: Knowledge of Python (and Matlab)