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Electrical and Computer Engineering

DEEP LEARNING

Deep learning allows the identification of objects in images, translating languages and driving cars autonomously. Deep Learning is rapidly gaining application across all industries due to the availability of adequate computing power (e.g., GPU’s and large data sets to train with. This is true from sensor data processing to database analytics to fraud detection in banks. Utah currently has a large number of unfilled well-paying jobs in this area. The deep learning certificate program provides education to engineering and science graduate students beyond those with computing background. In addition, the certificate program requires a graduate internship project with program’s industry partners.

Learn more about Deep Learning at the U

Suggested Coursework

MS Degree Requirements
Certificate Requirements

The following are suggested Programs of Study for a graduate student pursuing an ECE MS degree while earning a Deep Learning Certificate.

Traditional MS

3 Semesters   |   1-1.5 Years

These Programs of Study meet Coursework option requirements for a full-time student and can easily be adapted to the Project option. Students completing the Thesis option must meet with the Graduate Student Coordinator to create their academic plan.

Part-Time MS

*Graduate seminar credits may be completed in any order within a student’s first academic year in the program.

5 Semesters   |   2-2.5 Years

A schedule like this is for students who are currently working and don’t have the capacity to be full-time students but feel capable of managing a steady pace of courses (2 per semester).

Coursework

Semester Courses Credits
1 3 7.0*
2 3 7.0*
3 2 6.0
4 2 6.0
5 2 6.0
TOTAL 12 32.0

Summer Project

Semester Courses Credits
1 3 7.0*
2 3 7.0*
3 2 6.0
4 2 6.0
Summer Project 4.0
TOTAL 10 30.0

9 Semesters   |   4 Years

A reduced and extended part-time schedule of 8 semesters of coursework, with 1 course per term, and a summer project.

Year 1

Semester Credits
Fall
 4.0*
Spring
 4.0*

Year 2

Semester Credits
Fall
 3.0
Spring
 3.0

Year 3

Semester Credits
Fall
 3.0
Spring
 3.0
Summer
 4.0 – Project

Year 4

Semester Credits
Fall
 3.0
Spring
 3.0
TOTAL
 30.0

Default Supervisory Committee

The default committee will be determined by any additional area(s) of emphasis a student pursues with the ECE electives.

Priyank Kalla

Professor

VLSI systems: automated synthesis and optimization, validation and verification of digital VLSI systems, including: formal verification of RTL descriptions, new techniques to guide CNF-SAT search, using Groebner's proof systems for simplification of design verification and SAT solving, and design automation for optic/photonic logic

Pierre-Emmanuel Gaillardon

Associate Chair, Professor

Development of reconfigurable logic architectures and digital circuits exploiting emerging device technologies and novel EDA techniques.

Ken Stevens

Professor

VLSI, asynchronous circuit design and architecture, timing analysis, and formal verification