Core Courses & Electives
Students wishing to substitute any core or elective courses must seek approval from the ECE Graduate Program Office beforehand. Requests should justify the change and may need the student’s research advisor’s endorsement. Late substitution requests may be declined.
The department supports crosscutting specializations. Students with overlapping interests should collaborate with their research advisor to create a fivecourse list. This list should align with the guidelines for PhD Qualifying Exam Courses by Concentration. Before registering, they must secure approval from the Graduate Program Office. Retrospective requests are typically not favored without a strong justification.
Before taking the PhD Qualifying Examination, there are coursework prerequisites for each specialization area in the doctoral program.
Course Listing

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:509 Convex Optimization for Engineering Applications
332:521 Digital Signal Analytics
332:525 Optimum Signal Processing: Signal Process. & Machine Learning for Engineers
332:541 Stochastic Signals and Systems
332:542 Information Theory and Coding
332:545 Digital Communication Systems
332:548 Error Control Coding
332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
332:557 Quantum Computing and Communications AlgorithmsRestricted Mathematics Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
642:527 Methods of Applied Mathematics I
642:528 Methods of Applied Mathematics II
642:550 Linear Algebra and Applications
642:581 Graph Theory
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization
960:592 Theory of Probability
960:593 Theory of Statistics

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:509 Convex Optimization for Engineering Applications
332:530 Introduction to Deep Learning
332:531 Probabilistic Methods for Large Scale Signal Processing and Learning
332:532 Multimodal Machine Learning for Sensing Systems
332:533 Machine Learning for Inverse Problems
332:541 Stochastic Signals and Systems
332:543 Communications Networks I
332:544 Communications Networks II
332:546 Wireless Communication Technologies
332:568 Software Engineering Web Applications
332:573 Data Structures and Algorithms
198:512 Intro. to Data Structures & Alg. (equivalent to 332:573; credit given for only one)
198:513 Design and Analysis of Data Structures and AlgorithmsRestricted Mathematics Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
642:527 Methods of Applied Mathematics I
642:528 Methods of Applied Mathematics II
642:550 Linear Algebra and Applications
642:581 Graph Theory
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization
960:592 Theory of Probability
960:593 Theory of Statistics

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:509 Convex Optimization for Engineering Applications
332:515 Reinforcement Learning for Engineers
332:521 Digital Signal Analytics
332:525 Optimum Signal Processing: Signal Process. & Machine Learning for Engineers
332:531 Probabilistic Methods for Large Scale Signal Processing and Learning
332:533 Machine Learning for Inverse Problems
332:541 Stochastic Signals and Systems
332:542 Information Theory and Coding
332:545 Digital Communication Systems
332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
332:557 Quantum Computing and Communications AlgorithmsRestricted Mathematics Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
642:550 Linear Algebra and Applications
642:581 Graph Theory
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization
960:565 Applied Time Series Analysis
960:567 Applied Multivariate Analysis
960:592 Theory of Probability
960:593 Theory of Statistics

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:509 Convex Optimization for Engineering Applications
332:521 Digital Signal Analytics
332:525 Optimum Signal Processing: Signal Process. & Machine Learning for Engineers
332:530 Introduction to Deep Learning
332:532 Multimodal Machine Learning for Sensing Systems
332:541 Stochastic Signals and Systems
332:561 Machine Vision
332:590 Socially Cognizant Robotics
198:534 Computer Vision
198:536 Machine LearningRestricted Mathematics Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
960:5xx Any course in Statistics (960) at the 500 level or above
642:527 Methods of Applied Mathematics I
642:528 Methods of Applied Mathematics II
642:550 Linear Algebra and Applications
642:581 Graph Theory
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization 
Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:509 Convex Optimization for Engineering Applications
332:515 Reinforcement Learning for Engineers
332:530 Introduction to Deep Learning
332:531 Probabilistic Methods for Large Scale Signal Processing and Learning
332:532 Multimodal Machine Learning for Sensing Systems
332:533 Machine Learning for Inverse Problems
332:541 Stochastic Signals and Systems
332:549 Detection & Estimation Theory: Inference & Machine Learning for Engrs
332:561 Machine VisionRestricted Mathematics Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
642:527 Methods of Applied Mathematics I
642:528 Methods of Applied Mathematics II
642:550 Linear Algebra and Applications
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization
960:565 Applied Time Series Analysis
960:567 Applied Multivariate Analysis
960:592 Theory of Probability
960:593 Theory of Statistics

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:501 System Analysis
332:505 Control System Theory
332:506 Applied Controls
332:508 Digital Control Systems
332:509 Convex Optimization for Engineering Applications
332:510 Optimal Control Systems
332:512 Nonlinear Adaptive Control and Learning For Engineers
332:514 Stochastic Control Systems
332:515 Reinforcement Learning for Engineers
332:541 Stochastic Signals and Systems
650:504 Advanced Controls I (equivalent to 332:505; credit given for only one)
650:505 Advanced Controls IIRestricted Elective Courses
640:411 Mathematical Analysis I
640:412 Mathematical Analysis II
640:5xx Any course in Mathematics (640) at the 500 level or above
642:527 Methods of Applied Mathematics I
642:528 Methods of Applied Mathematics II
642:550 Linear Algebra and Applications
711:562 Nonlinear Optimization
711:685 Special Topics in Operations Research: Convex Analysis and Optimization
960:592 Theory of Probability
960:593 Theory of Statistics

Coursework Requirements
To be eligible for the Qualifying Exam, students must complete 3 Core courses, 1 Restricted Elective course, and 1 Restricted Mathematics Elective course. The combined GPA of the three Core and the Restricted Elective courses must be at least 3.75. Additionally, students must attain a grade of B+ or higher in the Restricted Mathematics Elective course. If students have received transfer credits for any of the prerequisite courses mentioned below, these credits cannot be used towards the Qualifying Exam requirement. However, students can replace them with additional Restricted Elective courses. Consequently:
 A student with transfer credit for 1 Core course can take 2 Core courses, 2 Restricted Elective courses, and 1 Restricted Mathematics Elective course.
 A student with transfer credit for 2 Core courses can take 1 Core course, 3 Restricted Elective courses, and 1 Restricted Mathematics Elective course.
 A student with transfer credit for 3 Core courses can take 3 Restricted Elective courses and 1 Restricted Mathematics Elective course.
Core Courses
332:543 Communication Networks I
332:563 Computer Architecture I
332:566 Introduction to Parallel and Distributed Computing
332:567 Software Engineering I
332:573 Data Structure and AlgorithmsRestricted Elective Courses
332:516 Cloud Computing and Big Data
332:518 Mobile Embedded Systems and OnDevice AI
332:530 Introduction to Deep Learning
332:544 Communication Networks II
322:561 Machine Vision
332:579 Advanced Topics in Computer Engineering (consult GD before registering)Restricted Mathematics Elective Courses
640:5xx Any course in Mathematics (640) at the 500 level or above
642:5xx Any course in Applied Mathematics (642) at the 500 level or above
960:5xx Any course in Statistics (960) at the 500 level or above 
Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:516 Cloud Computing and Big Data
332:543 Communications Networks I
332:544 Communications Networks II
322:561 Machine Vision
332:563 Computer Architecture I
332:566 Introduction to Parallel and Distributed Computing
332:567 Software Engineering I
332:568 Software Engineering for Web Applications
332:569 Database System Engineering
332:571 Virtual Reality
332:573 Data Structures and AlgorithmsRestricted Mathematics Elective Courses
640:5xx Any course in Mathematics (640) at the 500 level or above
642:5xx Any course in Applied Mathematics (642) at the 500 level or above
960:5xx Any course in Statistics (960) at the 500 level or above

Coursework Requirements
To be eligible for the Qualifying Exam, students are required to complete 4 Core courses with a collective GPA of at least 3.75 and 1 Mathematics Elective course with a grade of B+ or higher from the provided lists.
Core Courses
332:507 Security Engineering
332:530 Introduction to Deep Learning
332:542 Information Theory and Coding
332:544 Communication Networks II
332:548 Error Control Coding
332:557 Quantum Computing and Communications Algorithms
332:567 Software Engineering
332:573 Data Structures & Algorithms
332:579 Advanced Topics in Computer Engineering: Hardware and Systems Security
198:546 Computer System SecurityRestricted Mathematics Elective Courses
640:5xx Any course in Mathematics (640) at the 500 level or above
642:5xx Any course in Applied Mathematics (642) at the 500 level or above
960:5xx Any course in Statistics (960) at the 500 level or above

Coursework Requirements
To be eligible for the Qualifying Exam, students must complete 3 Core courses, 1 Restricted Elective course, and 1 Restricted Mathematics Elective course. The combined GPA of the three Core and the Restricted Elective courses must be at least 3.75. Additionally, students must attain a grade of B+ or higher in the Restricted Mathematics Elective course. If students have received transfer credits for any of the prerequisite courses mentioned below, these credits cannot be used towards the Qualifying Exam requirement. However, students can replace them with additional Restricted Elective courses. Consequently:
 A student with transfer credit for 1 Core course can take 2 Core courses, 2 Restricted Elective courses, and 1 Restricted Mathematics Elective course.
 A student with transfer credit for 2 Core courses can take 1 Core course, 3 Restricted Elective courses, and 1 Restricted Mathematics Elective course.
 A student with transfer credit for 3 Core courses can take 3 Restricted Elective courses and 1 Restricted Mathematics Elective course.
Core Courses
332:580 Electric Wave and Radiation
332:581 Introduction to Solid State Electronics
332:583 Semiconductor Devices I • 332:587 Transistor Circuit Design
332:599 Advanced Topics in Solid State Electronics: Semiconductors for AI
332:599 Advanced Topics in Solid State Electronics: Microelectronic Processing
635:503 Theory of SolidState MaterialsRestricted Elective Courses
332:574 Computer Aided Digital VLSI Design
332:578 Deep Submicron VLSI Design
332:584 Semiconductor Devices II
332:589 RF Integrated Circuit Design
332:591 OptoElectronics I
332:599 Advanced Topics in Solid State Electronics: Biosensing and Bioelectronics
750:501 Quantum Mechanics I
750:601 Solid State Physics IRestricted Mathematics Elective Courses
640:5xx Any course in Mathematics (640) at the 500 level or above
642:5xx Any course in Applied Mathematics (642) at the 500 level or above
960:5xx Any course in Statistics (960) at the 500 level or above