Webinar: Machine Learning: From Data to Decisions
This is a 60-minute webinar with Prof. Devavrat Shah to learn more about the upcoming Machine Learning: From Data to Decisions (Online) Program, followed by a Q&A session. Register here.
In the online program, Machine Learning: From Data to Decisions, you’ll learn the most practical applications of machine learning, and explore a variety of relevant case studies and methods. Machine learning has profound effects in many different industries, from financial services to retail to advertising. It is fast becoming a fundamental tool for making better decisions in business—decisions driven by data, not gut feelings or guesswork. There are no prerequisites for this course, though knowledge of basic statistics is helpful.
Visit the Machine Learning: From Data to Decisions course page.
Prof. Devavrat Shah
Devavrat Shah is a professor with the department of electrical engineering and computer science at MIT. He is a member of the Laboratory for Information and Decision Systems (LIDS) and Operations Research Center (ORC), and the Director of the newly formed Statistics and Data Center in Institute for Data, Systems, and Society (IDSS). His research focus is on the theory of large complex networks, which includes network algorithms, stochastic networks, network information theory, and large-scale statistical inference. Professor Shah was awarded the first ACM SIGMETRICS Rising Star Award 2008 for his work on network scheduling algorithms. He received the 2010 Erlang Prize from INFORMS, which is given to a young researcher for outstanding contributions to applied probability. He is currently an associate editor of Operations Research.