- Schedule of Classes - February 16, 2020 7:14PM EST
- Course Catalog - February 16, 2020 7:15PM EST
Course information provided by the Courses of Study 2019-2020.
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.
Credits and Grading Basis
3 Credits Graded(Letter grades only)
Class Number & Section Details
- TRUpson Hall 216
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