MIT Prof. Jinhua Zhao will be delivering this 2-day course in Tokyo, Japan. This course will focus on two forces driving the future of urban mobility: behavior and computation. We use a behavioral lens to examine mobility technologies such as automation, connectivity, sharing, and electrification, and demonstrate the power of integrating behavioral and computational thinking. We observe that travel behavior is emotional, social, and perceptual, and mobility service can be predictive, individualized, experimental, and multimodal.
We are in a world where the new technologies of Cloud, IoT, AI/Machine Learning, and Cloud Data-Stacks are re-architecting how organizations operate. DevOps and DataOps driven organizations move thousands of times faster than others.
To master this “Tsunami” of data we need to leverage new tools and new data analysis techniques. We need to master the new Cloud datastores where we can query petabytes of data in seconds.
We need to understand how to leverage the new data-in-motion pipelines to make business decisions in seconds rather than days or weeks.
MIT Professional Education is pleased to offer the Professional Certificate Program in Machine Learning & Artificial Intelligence. MIT has played a leading role in the rise of AI and the new category of jobs it is creating across the world economy. Our goal is to ensure businesses and individuals have the education and training necessary to succeed in the AI-powered future. This certificate guides participants through the latest advancements and technical approaches in artificial intelligence technologies such as natural language processing, predictive analytics, deep learning, and algorithmic methods to further your knowledge of this ever-evolving industry.
La implementación eficaz de la tecnología requiere de una cuidadosa planificación, evaluación y gestión para garantizar que se aplique lo que más beneficie a tu organización, en función del personal, producto, servicio y objetivos.
Champion the current complexities of technology implementation for your organization.
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.
Stuart Madnick
Dr. Stuart E. Madnick is the John Norris Maguire (1960) Professor of Information Technology, Emeritus in the MIT Sloan School of Management, as well as a Professor of Engineering Systems in the MIT School of Engineering. He is also Founding Director of Cybersecurity—MIT Sloan’s consortium for improving critical infrastructure cybersecurity—and the Co-Head of the MIT Information Quality (MITIQ) Program. He is the author or co-author of more than 400 publications, including Computer Security, one of the first books ever published on the topic of cybersecurity.
MIT Professional Education’s No Code AI and Machine Learning: Building Data Science Solutions Program, with a curriculum developed and taught by MIT faculty, can help you craft effective business solutions without writing a single line of code.
This course is designed to help you learn and apply advanced data tools for IIoT and smart manufacturing. The curriculum ranges from foundational concepts to in-depth, hands-on activities using production data, and covers a variety of cutting-edge approaches such as deep reinforcement learning control, encryption for data outsourcing, and predictive data analytics algorithms.
Tim Kraska
Tim Kraska is an Associate Professor of Electrical Engineering and Computer Science in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), where he co-leads the Data Systems Group. He is also the Founding Co-Director of MIT Data System and AI Lab (DSAIL).