Computer Science

Introduction to Deep Learning

Introduction to Deep Learning
Theory and practice of modern deep learning and associated software frameworks. A broad look at the field, drawing on material from machine vision, machine translation, dynamical systems, audio processing, neural computing and human perception. Supporting mathematical concepts are also covered, including linear algebra, stochastic optimization, and hardware acceleration.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesC S 312; or C S 312 & MATH 213 & MATH 215
 NoteStudents are allowed 1 repeat of each C S undergraduate course (all 100-, 200-, 300- or 400-level courses). This includes all students who received any grade including those who withdraw (receive a "W" grade) from a C S course. Students must wait 1 semester/term before being allowed to take a course they have failed twice. Petitions for exceptions to the policy can be completed at
 ProgramsContaining C S 474
Course Outcomes: 

Please contact the individual department for outcome information.