Machine Learning in Business Management:Stochastic dynamic programming and Reinforcement learning |
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Location: UCSC Silicon Valley Center at NASA (Mountain View): Room 2069 UCSC Main campus: Room , Baskin Engineering Room 156
Time: 6pm-9:30pm Tuesday, Instructor at Santa Cruz, Telecast to SVC
Instructors: Ram Akella (akella (at) soe.ucsc.edu)
TA: Jyotsna Gangwar (jyotsna (at) soe.ucsc.edu) TA hour at SVC: 5:15-5:45pm Tuesday (SVC 2095) TA hour at Santa Cruz Campus: 12:00pm - 1:00pm Wednesday
WebCT (for homework submission)
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Course Objectives: Train students to write research paper which is industrially applicable. The first half of the course will be lecture based and cover the theory of stochastic dynamic programming and reinforcement learning. The second half of the course will be based more on discussions and presentations about related papers covering different application areas of machine learning and business management.
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References: Reinforcement Learning: - Reinforcement learning: An Introduction by Richard s. Sutton and Andrew G. Barto online (html), online (cruzcat) (Main textbook)
- Dynamic Programming and Optimal Control by Dimitri P. Bertsekas
Data Mining: - Pattern Classification, 2nd Edition: Duda, Hart, and Stork, Wiley, 2001 (Strongly recommended) - The Elements of Statistical Learning: Hastie, Tibshirani, and Friedman, Springer, 2001
Business Management: - Marketing Engineering, Gary Lilien and Arvind Rangaswamy, Prentice Hall, 2003 - Marketing Models, Gary Lilien, Philip Kotler, Sridhar Moorthy, Prentice Hall, 1992
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Grading: Grades will be based on:
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