Machine learning is the “engine” that powers the impressive capabilities of modern AI, such as self-driving cars and ChatGPT. 

This talk will lay out the mathematical foundations of machine learning, and use practical examples to point out best practices as well as pitfalls in the use of machine learning  in applications that relate to predictive models. Finally, we provide resources for getting up to speed quickly in the field. 

Bio: Christopher Thron is associate professor in the Department of Science and Mathematics at Texas A&M University -Central Texas. Previously he was a communications systems engineer with NEC America, Motorola, and Freescale. He has Ph.D. degrees in mathematics (from University of Wisconsin) and physics (University of Kentucky). His current research focus areas include machine learning, operations research, stochastic optimization, agent-based modelling and algorithm design. Applications he has worked on include communications systems, target tracking, scheduling, epidemiological and social modeling, and public health statistics. He has 50+ journal publications, 3 books, and 9 patents He has participated extensively in collaborative research in Africa, and has been supported by the U.S. Fulbright program, International Mathematician’s Union, and the U.S. Air Force Research Laboratory.