An in-depth introduction to time series analysis, from learning structured models to predictions and reinforcement learning, with hands-on projects - Part of the MITx MicroMasters program in Statistics and Data Science.
An in-depth introduction to time series analysis, from learning structured models to predictions and reinforcement learning, with hands-on projects - Part of the MITx MicroMasters program in Statistics and Data Science.
If you have specific questions about this course, please contact us at sds-mm@mit.edu.
Prof. Dahleh is internationally known for his fundamental contributions to robust control theory, computational methods for controller design, the interplay between information and control, the fundamental limits of learning and decision in networked systems, and the detection and mitigation of systemic risk in interconnected and networked systems.
Professor Shah’s research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including resource allocation in communications networks, inference and learning on graphical models, and algorithms for social data processing including ranking, recommendations and crowdsourcing. Within the broad context of networks, his work spans a range of areas across electrical engineering, computer science and operations research.
Dr. Roozbehani’s main research interests surround the theory and applications of control and optimization, particularly in large-scale and distributed systems:
Karene Chu received her Ph.D. in mathematics from the University of Toronto in 2012. Since then she has been a postdoctoral fellow first at the University of Toronto/Fields Institute, and then at MIT, with research focus on knot theory. She has taught single and multi-variable calculus, and linear algebra at the University of Toronto where she received a teaching award.