CS 6685 - Deep Learning Theory and Applications

Institution:

Utah State University

Subject:

Computer Science

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. Feedforward, convolutional, generative adversarial, and recurrent networks are discussed, as well as autoencoders, other modern architectures, and training techniques. Additional coursework is required for those enrolled in the graduate-level course Prerequisite(s): MATH 1220; MATH 2270 or instructor permission; STAT 3000 or MATH 5710 Prerequisite Recommendation(s): MATH 2210 and MATH 5710 are recommended. Programming experience, preferably in Python, is also strongly recommended Repeatable for credit: N 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

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