STAT 5685 - Deep Learning Theory and Applications

Institution:

Utah State University

Subject:

Statistics

Description:

This course takes a principled and hands-on approach to deep learning with neural networks, covering machine learning basics, backpropagation, stochastic gradient descent, regularization, and universality. Topics include CNNs, GANs, RNNs, GCNs, autoencoders, transformers, and other modern architectures and training techniques. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite Recommendation(s): The following courses or their equivalents are necessary to succeed in this course: MATH 1220; MATH 2270 or MATH 5645/STAT 5645 or MATH 6645/STAT 6645; STAT 3000 or MATH 5710; Programming experience, preferably in Python, is also necessary to succeed in this course Dual-listed as: STAT 6685 Repeatable for credit: No Grade Mode: Standard

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.usu.edu

Phone Number:

(435) 797-1000

Regional Accreditation:

Northwest Commission on Colleges and Universities

Calendar System:

Semester

General Education

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