ORIE 6590
Last Updated
- Schedule of Classes - January 7, 2018 7:14PM EST
- Course Catalog - January 7, 2018 7:15PM EST
Classes
ORIE 6590
Course Description
Course information provided by the Courses of Study 2017-2018.
This course develops theories and algorithms for optimal sequential decision making under uncertainty. The emphasis will be on approximate algorithms to deal with large-scale decision models that can be highly uncertain. Various bounding techniques in recent literature will be covered. The course will intersect with traditional topics such as Markov decision processes, reinforcement learning, and bandit problems.
When Offered Fall.
Prerequisites/Corequisites Prerequisite: ORIE 6500 or equivalent.
Regular Academic Session. Combined with: ORIE 6590
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Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
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Class Number & Section Details
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Meeting Pattern
- MWF Frank H T Rhodes Hall 571
- Aug 22 - Aug 28, 2017
Instructors
Dai, J
- MW Frank H T Rhodes Hall 571
- Sep 1 - Sep 6, 2017
Instructors
- MF Frank H T Rhodes Hall 571
- Sep 11 - Sep 25, 2017
Instructors
- MWF Frank H T Rhodes Hall 571
- Oct 11 - Oct 16, 2017
Instructors
- F Frank H T Rhodes Hall 571
- Oct 20 - Oct 27, 2017
Instructors
- MF Frank H T Rhodes Hall 571
- Oct 30 - Nov 17, 2017
Instructors
Regular Academic Session. Combined with: ORIE 6590
-
Credits and Grading Basis
3 Credits Stdnt Opt(Letter or S/U grades)
-
Class Number & Section Details
-
Meeting Pattern
-
MWF
Bloomberg Center 398
Cornell Tech - Aug 22 - Aug 28, 2017
Instructors
Dai, J
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MW
Bloomberg Center 398
Cornell Tech - Sep 1 - Sep 6, 2017
Instructors
-
MF
Bloomberg Center 398
Cornell Tech - Sep 11 - Sep 25, 2017
Instructors
-
MWF
Bloomberg Center 398
Cornell Tech - Oct 11 - Oct 16, 2017
Instructors
-
F
Bloomberg Center 398
Cornell Tech - Oct 20 - Oct 27, 2017
Instructors
-
MF
Bloomberg Center 398
Cornell Tech - Oct 30 - Nov 17, 2017
Instructors
-
MWF
Bloomberg Center 398
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Additional Information
Instruction Mode: Distance Learning - WWW
Taught in NYC. Enrollment limited to: Cornell Tech PhD students. Taught via distance learning, streamed from Ithaca.
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