ORIE 4742
Last Updated
- Schedule of Classes - September 9, 2021 7:14PM EDT
- Course Catalog - September 9, 2021 7:15PM EDT
Classes
ORIE 4742
Course Description
Course information provided by the Courses of Study 2020-2021.
This course is about building and understanding machine learning models for scientific and financial applications. It will cover foundational aspects of information theory and probabilistic inference as they relate to model construction and deep learning. Topics include hamming codes, repetition codes, entropy, mutual information, Shannon information, channel capacity, likelihood functions, Bayesian inference, graphical models, and deep neural networks. The section on deep neural networks will consider fully connected, convolutional, recurrent, and LSTM networks, generative adversarial training, and variational autoencoders.
When Offered Spring.
Prerequisites/Corequisites Prerequisite: ORIE 3500, MATH 2940 or equivalent, CS 2110 or equivalent, exposure to statistical machine learning at the level of ORIE 4740, ORIE 4741 or equivalent or permission of the instructor.
Regular Academic Session.
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Credits and Grading Basis
3 Credits Graded(Letter grades only)
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Class Number & Section Details
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Meeting Pattern
- MW Mann Library 107
- Feb 8 - May 14, 2021
Instructors
Banerjee, S
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Additional Information
Instruction Mode: In Person
Enrollment limited to students who are able to attend in-person classes in the Ithaca area.
Regular Academic Session.
-
Credits and Grading Basis
3 Credits Graded(Letter grades only)
-
Class Number & Section Details
-
Meeting Pattern
- MW Online Meeting
- Feb 8 - May 14, 2021
Instructors
Banerjee, S
-
Additional Information
Instruction Mode: Online
For instructions on requesting permission, send brief email to Heidi Russell (hjr27).
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