Decision Making in Large Scale Systems OpenCourseWare: A Free MIT Graduate Study Course on Dynamic Programming

Published Jan 26, 2009

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Students taking part in the 'Decision Making in Large Scale Systems' OpenCourseWare project will learn about the fundamentals of large-scale dynamic programming. Specific topics include simulation-based algorithms, policy search methods and dynamic programming algorithms. A background in mechanical engineering and programming is required for the successful completion of this course.

Decision Making in Large Scale Systems: Course Specifics

Degree Level Free Audio Video Downloads
Graduate Yes No No Yes

Lecture Notes Study Materials Tests/Quizzes
Yes Yes No

Decision Making in Large Scale Systems: Course Description

'Decision Making in Large Scale Systems' is taught by Professor Daniela Pucci De Farias and designed for graduate students with an interest in dynamic programming. Some of the specific topics covered in the course include dynamic programming, Markov decision processes, simulation-based methods, value function approximation, policy search methods and online learning and games. Students will complete problem set, read assigned texts and lecture notes and write a 10-15 page paper on theory or algorithms. This course was originally taught in a lecture format in the spring of 2004.

Lecture notes and problem sets are available for download online for free. If you are interested in taking this course, please visit the decision making in large scale systems course page.

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