- Schedule of Classes - June 18, 2017 7:14PM EDT
- Course Catalog - June 14, 2017 7:15PM EDT
Course information provided by the Courses of Study 2016-2017.
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 Prerequisites: ORIE 3500 and MATH 2940 or equivalent. Programming experience at the level of 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
- MWF Hollister Hall 320
Disabled for this roster.