Departmental Advisors
Trans./University
N. Cotter
MEB 2270
Undergraduate Studies Office/Director of Advising
A. Rasmussen
MEB 2268
Electrical Engineering Undergraduate Advisor
Brandon Riddle
MEB 2266B
Computer Engineering Undergraduate Advisor
Maddie Porter
MEB 2266A
Graduate Advisor
Liz Rowberry
MEB 2266C
Academic Advising Coordinator
John Bolke
MEB 2110D
BS/MS Advisor
A. Verkler
MEB 2102
Departmental Notes
For course descriptions and pre-requisite information click on the subject column next to the appropriate catalog number.
ALL STUDENTS TAKING ELECTRONIC ENGINEERING LABS MUST PURCHASE ELECTRONIC COMPONENTS. For course descriptions and pre-requisite information click on the subject column next to the appropriate catalog number. THIS DEPARTMENT ENFORCES UNDERGRADUATE PREREQUISITES. Please note that the registration system may not factor in transfer work when determining if prerequisites have been met. If you are unable to register for a course and think you have met the prerequisite(s), please fill out the Registration Permission Code request form found on our website at http://www.ece.utah.edu/ . You may be administratively dropped from a course if the prerequisite has not been met.
Attention: Classroom assignments may change between the time you
register and when classes begin. Please check your class schedule for the latest classroom location
information before attending class.
ECE 3980 - 090 Independent Project
ECE 3980 - 090 Independent Project
- Class Number: 11237
- Instructor: DAVIES, JON
- Component: Special Topics
- Type: Online
- Units: 1.0
- Requisites: Yes
- Wait List: No
- Seats Available: 6
ECE 3990 - 090 Cooperative Educ
Students are expected to attend their internships as contracted with their employers. https://www.ece.utah.edu/co-op-internship
ECE 3990 - 090 Cooperative Educ
- Class Number: 1440
- Instructor: RASMUSSEN, ANGELA
- Component: Practicum
- Type: Online
- Units: 1.0 - 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: -9
Students are expected to attend their internships as contracted with their employers. https://www.ece.utah.edu/co-op-internship
ECE 5201 - 090 Phys of Nano-Elec Dev
This is an online course, which does not have a specific meeting time or location throughout the semester. For additional information, please visit https://online.utah.edu/about-online-learning/. The course fee covers digital course materials through the Inclusive Access program. Students may request to opt out here: https://portal.verba.io/utah/login
ECE 5201 - 090 Phys of Nano-Elec Dev
- Class Number: 9805
- Instructor: TABIB-AZAR, MASSOOD
- Component: Lecture
- Type: Online
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Fees: $36.71
- Seats Available: 10
This is an online course, which does not have a specific meeting time or location throughout the semester. For additional information, please visit https://online.utah.edu/about-online-learning/. The course fee covers digital course materials through the Inclusive Access program. Students may request to opt out here: https://portal.verba.io/utah/login
ECE 5331 - 090 Optics for Energy
This section is open to ECE, MSE and ME EN students who wish to take an online section
ECE 5331 - 090 Optics for Energy
- Class Number: 13093
- Instructor: MENON, RAJESH
- Component: Lecture
- Type: Online
- Units: 3.0
- Wait List: No
- Seats Available: 3
This section is open to ECE, MSE and ME EN students who wish to take an online section
ECE 5530 - 090 Digital Signal Process
ECE 5530 - 090 Digital Signal Process
- Class Number: 20214
- Instructor: NATEGH, NEDA
- Component: Lecture
- Type: Online
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 21
ECE 5960 - 090 Mathematical Tools Neural Data
Mathematical tools for neural data analysis and modeling The course covers a set of mathematical and statistical methods that are fundamental for analyzing and modeling neural/cognitive data and neural signal and information processing, which are practiced through extensive computational exercises. The tentative topics include linear algebra, least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, classification, linear discriminants, clustering, simple models of neural spike generation, white noise (reverse-correlation) analysis, and if time permits information theory, generalized linear models. The course is intended for students from quantitative backgrounds, including engineering, math, statistics, computer science, physics, neuroscience, psychology.
ECE 5960 - 090 Mathematical Tools Neural Data
- Class Number: 12287
- Instructor: NATEGH, NEDA
- Component: Special Topics
- Type: Online
- Units: 3.0 - 4.0
- Requisites: Yes
- Wait List: No
- Seats Available: 17
Mathematical tools for neural data analysis and modeling The course covers a set of mathematical and statistical methods that are fundamental for analyzing and modeling neural/cognitive data and neural signal and information processing, which are practiced through extensive computational exercises. The tentative topics include linear algebra, least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, classification, linear discriminants, clustering, simple models of neural spike generation, white noise (reverse-correlation) analysis, and if time permits information theory, generalized linear models. The course is intended for students from quantitative backgrounds, including engineering, math, statistics, computer science, physics, neuroscience, psychology.
ECE 6331 - 090 Optics for Energy
This is an online course, which does not have a specific meeting time or location throughout the semester. For additional information, please visit https://online.utah.edu/about-online-learning/. This section is open to ECE, MSE and ME EN students who wish to take an online section
ECE 6331 - 090 Optics for Energy
- Class Number: 9835
- Instructor: MENON, RAJESH
- Component: Lecture
- Type: Online
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 4
This is an online course, which does not have a specific meeting time or location throughout the semester. For additional information, please visit https://online.utah.edu/about-online-learning/. This section is open to ECE, MSE and ME EN students who wish to take an online section
ECE 6530 - 090 Digital Signal Process
ECE 6530 - 090 Digital Signal Process
- Class Number: 15895
- Instructor: NATEGH, NEDA
- Component: Lecture
- Type: Online
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 4
ECE 6550 - 090 Adaptive Filters
ECE 6550 - 090 Adaptive Filters
- Class Number: 20533
- Instructor: FARHANG, BEHROUZ
- Component: Lecture
- Type: Online
- Units: 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 17
ECE 6960 - 090 Mathematical Tools Neural Data
Mathematical tools for neural data analysis and modeling The course covers a set of mathematical and statistical methods that are fundamental for analyzing and modeling neural/cognitive data and neural signal and information processing, which are practiced through extensive computational exercises. The tentative topics include linear algebra, least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, classification, linear discriminants, clustering, simple models of neural spike generation, white noise (reverse-correlation) analysis, and if time permits information theory, generalized linear models. The course is intended for students from quantitative backgrounds, including engineering, math, statistics, computer science, physics, neuroscience, psychology. This class is designed as a 4 credit hour course. To enroll in 3 credit hours contact instructor for consent.
ECE 6960 - 090 Mathematical Tools Neural Data
- Class Number: 12288
- Instructor: NATEGH, NEDA
- Component: Special Topics
- Type: Online
- Units: 3.0 - 4.0
- Requisites: Yes
- Wait List: No
- Seats Available: 15
Mathematical tools for neural data analysis and modeling The course covers a set of mathematical and statistical methods that are fundamental for analyzing and modeling neural/cognitive data and neural signal and information processing, which are practiced through extensive computational exercises. The tentative topics include linear algebra, least-squares regression, eigen-analysis and PCA, linear shift-invariant systems, convolution, Fourier transforms, Nyquist sampling, basics of probability and statistics, hypothesis testing, model comparison, bootstrapping, estimation and decision theory, signal detection theory, classification, linear discriminants, clustering, simple models of neural spike generation, white noise (reverse-correlation) analysis, and if time permits information theory, generalized linear models. The course is intended for students from quantitative backgrounds, including engineering, math, statistics, computer science, physics, neuroscience, psychology. This class is designed as a 4 credit hour course. To enroll in 3 credit hours contact instructor for consent.
ECE 6960 - 091 Professional Development
Career, Research, and Network Development for Graduate Students The purpose of this course is for graduate students to explore a variety of career options (industry, academia, national labs, policy), and the skills that can lead to strong career success and technical leadership. The course covers Career Development, Research Development, and Network/Mentoring Development and is variable credit, allowing students to select assignments from each area to best fit their interests and needs. Career Development includes creation of an Individual Development Plan (IDP), team and leadership skills, preparation for career fairs, interviews with stakeholders to explore career options, and more. Research Development is based on the Lean Canvas approach which encourages research students to explore their research area not only in the traditional way (professional literature) but also business and patent literature, and direct interviews/visits with professionals working in their field who may later be users of their research output. Network and Mentoring Development helps students develop a formal mentoring plan, find people to help guide their individual areas of growth, and learn skills to effectively engage with mentors and their professional network throughout their careers.
ECE 6960 - 091 Professional Development
- Class Number: 17551
- Instructor: FURSE, CYNTHIA
- Component: Special Topics
- Type: Online
- Units: 0.5 - 3.0
- Requisites: Yes
- Wait List: No
- Seats Available: 35
Career, Research, and Network Development for Graduate Students The purpose of this course is for graduate students to explore a variety of career options (industry, academia, national labs, policy), and the skills that can lead to strong career success and technical leadership. The course covers Career Development, Research Development, and Network/Mentoring Development and is variable credit, allowing students to select assignments from each area to best fit their interests and needs. Career Development includes creation of an Individual Development Plan (IDP), team and leadership skills, preparation for career fairs, interviews with stakeholders to explore career options, and more. Research Development is based on the Lean Canvas approach which encourages research students to explore their research area not only in the traditional way (professional literature) but also business and patent literature, and direct interviews/visits with professionals working in their field who may later be users of their research output. Network and Mentoring Development helps students develop a formal mentoring plan, find people to help guide their individual areas of growth, and learn skills to effectively engage with mentors and their professional network throughout their careers.