This course is ideal for advanced graduate students who are interested in applying novel research concepts to their own work. Familiarity with Python is helpful, but not required. A solid foundation in linear systems (at the level of ESE 500), probability theory (at the level of ESE 530), and optimization (at the level of ESE 605), as well as mathematical maturity (comfort with reading and writing proofs) is required. This is an advanced theory-intensive course. The course will also expose students to the ethical considerations that need to be considered when designing learning algorithms that interact with and are placed in feedback with the world. Topics of study will include learning models of dynamical systems, using these models to robustly meet performance objectives, optimally refining models to improve performance, and verifying the safety of machine learning enabled control systems. We will investigate machine learning and data-driven algorithms that interact with the physical world, with an emphasis on a holistic understanding of the interplay between concepts from control theory (e.g., feedback, stability, robustness) and machine learning (e.g., generalization, sample-complexity). This course will provide students an introduction to the emerging area at the intersection of machine learning, dynamics, and control. We will be posting Zoom links/passcodes on Piazza approximately 30min before lecture to prevent Zoom bombing. On Canvas, there will be a link to Piazza, please register there as well. Office hours: NM: Tu/Th 5:00-6:00pm ET, Levine 374 and on Zoom (check Canvas for Link/Passcode), SC: We/Fr 10:00-11:00am ET on Zoom (Check Canvas for Link/Passcode)Ĭanvas: We will be using Canvas to manage class logistics. Beyond showing basic respect to the instructor and your classmates, no requirements (e.g., cameras must be on, you may not watch from bed, no eating, etc.) will be asked of those tuning in via Zoom. You may choose to attend the live recordings or watch asynchronously. Lectures will be recorded live and posted to Canvas afterwards. Teaching assistant: Shaoru Chen ( Tu/Th 3:30-4:45pm ET, Moore 212 and on Zoom (check Canvas for Link/Passcode). Instructor: Nikolai Matni ( Assistant Professor, ESE Department ESE 618, Fall 2021 – Learning for Dynamics and Control
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