John R. Williams
Lead Instructor
John R. Williams is a Professor of Information Engineering and Civil and Environmental Engineering at MIT. His area of specialty is large scale computer analysis applied to both physical systems and to information.
Stefanie Jegelka
Lead Instructor
Stefanie Jegelka is an X-Consortium Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, where she is a member of CSAIL, and affiliated with IDSS.
Tommi S. Jaakkola
Tommi Jaakkola is a Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. He is also a member of the Computer Science and Artificial Intelligence Laboratory. His work pertains to inferential, algorithmic and estimation questions in machine learning, including large scale probabilistic distributed inference, deep learning, and causal inference.
Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology.
Examine how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied to produce powerful results, even when using unstructured data. In this highly interactive course, you’ll gain insights into what kinds of problems these methods can and cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications, particularly in the healthcare field.
Abel Sanchez
Lead Instructor
Participating Instructor
Dr. Abel Sanchez is the Executive Director of MIT's Geospatial Data Center, pioneering advancements in IoT, Big Data, AI, and Cybersecurity. He architected "The Internet of Things" global network and data analytics platforms for major corporations like SAP, Ford, Johnson & Johnson, Accenture, Shell, Exxon Mobil, and Boeing. In cybersecurity, Dr. Sanchez designed Cyber Ranges for the Department of Defense and a password firewall for IARPA. His machine learning work includes developing fraud detection frameworks for Accenture. Over the past three years, he has focused on Generative AI, building assistants, agents, and intelligence for sectors such as retail, finance, and government. Dr. Sanchez has also led e-Education software for Microsoft and co-founded the Accenture Technology Academy. He teaches MIT courses on cybersecurity, engineering computation, and data science, has produced over 1000 educational videos, and has taught professionals in 106 countries. He is a distinguished faculty member at MIT Professional Education, specializing in digital transformation.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.
Vivienne Sze
Participating Instructor
Vivienne Sze is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and leads the Research Lab of Electronics’ Energy-Efficient Multimedia Systems research group. Her group works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things.