Core Courses & Electives
The core and elective courses are tailored for various MS program specializations. Students wishing to substitute any of the core or elective courses within their chosen specialization with an unlisted course should formally approach the Graduate Program Office. All such requests should be accompanied by a rationale and, when relevant, an endorsement from the student’s thesis advisor.
For certain specializations, some “Advanced Topics” courses—explicitly highlighted below—may qualify for credit towards that specialization. Students should liaise with the Graduate Director (GD) to determine if a particular course topic aligns with the intended concentration.
It’s essential to note that this appendix does not explicitly cater to students with cross-cutting specializations. Such students should engage with their thesis advisor (if applicable) and the Graduate Director to develop an alternate study plan.
Note that the courses listed below reflect the 2024 handbook. Please refer to the appropriate handbook based on when you entered the program.
Course Listing
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Students pursuing the MS degree with a specialization in Communications are required to take at least 3 Core courses and 3 Restricted Elective courses from the subsequent lists. In the lists provided below, courses from the ECE department, which count towards the ECE residency requirement, are bolded. Meanwhile, courses from outside departments are presented in regular typeface.
Core Courses
- 332:541 Stochastic Signals and Systems
- 332:542 Information Theory and Coding
- 332:543 Communication Networks I
- 332:544 Communication Networks II
- 332:545 Digital Communication Systems
- 332:546 Wireless Communications Technologies
- 332:548 Error Control Coding
- 332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
Restricted Elective Courses
- 332:501 System Analysis
- 332:505 Control System Theory
- 332:506 Applied Controls
- 332:509 Convex Optimization for Engineering Applications
- 332:515 Reinforcement Learning for Engineers
- 332:519 Advanced Topics in System Engineering (consult GD before registering)
- 332:521 Digital Signal Analytics
- 332:525 Optimum Signal Processing: Signal Process. & Machine Learning for Engrs
- 332:539 Advanced Topics in Digital Signal Processing
- 332:557 Quantum Computing and Communications Algorithms
- 332:558 Quantum Computing and Information Systems
- 332:559 Advanced Topics in Communications Engineering
- 640:411 Mathematical Analysis I
- 640:412 Mathematical Analysis II
- 642:550 Linear Algebra and Applications
- 642:581 Graph Theory
- 643:573 Numerical Analysis I
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Students pursuing the MS degree with a specialization in Signal and Information Processing are required to take at least 3 Core courses and 3 Restricted Elective courses from the subsequent lists. In the lists provided below, courses from the ECE department, which count towards the ECE residency requirement, are bolded. Meanwhile, courses from outside departments are presented in regular typeface.
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
- 332:541 Stochastic Signals and Systems
- 332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
- 332:557 Quantum Computing and Communications Algorithms
- 332:561 Machine Vision
Restricted Elective Courses
- 332:501 System Analysis
- 332:505 Control System Theory
- 332:506 Applied Controls
- 332:510 Optimal Control System
- 332:512 Nonlinear Adaptive Control and Learning For Engineers
- 332:515 Reinforcement Learning for Engineers
- 332:519 Advanced Topics in System Engineering (consult GD before registering)
- 332:532 Multimodal Machine Learning for Sensing Systems
- 332:533 Machine Learning for Inverse Problems
- 332:539 Advanced Topics in Digital Signal Processing
- 332:542 Information Theory and Coding
- 332:558 Quantum Computing and Information Systems
- 332:579 Advanced Topics in Computer Engineering (consult GD before registering)
- 640:411 Mathematical Analysis I
- 640:412 Mathematical Analysis II
- 642:550 Linear Algebra and Applications
- 642:581 Graph Theory
- 643:621 Mathematical Finance I
- 643:622 Mathematical Finance II
- 954:596 Regression and Time Series Analysis for Data Science
- 960:565 Applied Time Series Analysis
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Students pursuing the MS degree with a specialization in Signal and Information Processing are required to take at least 3 Core courses and 3 Restricted Elective courses from the subsequent lists.
Core Courses
332:443 Machine Learning for Engineers (or its corresponding cross-listed course)
332:509 Convex Optimization for Engineering Applications
332:515 Reinforcement Learning for Engineers
332:530 Introduction to Deep learning
332:541 Stochastic Signals and Systems
332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
332:561 Machine Vision
Restricted Elective Courses
332:505 Control System Theory
332:516 Cloud Computing and Big Data
332:518 Mobile Embedded Systems and On-Device AI
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
332:532 Multimodal Machine Learning for Sensing Systems
332:533 Machine Learning for Inverse Problems
332:542 Information Theory and Coding
332:557 Quantum Computing and Communication Algorithms
332:558 Quantum Computing and Information Systems
332:566 Introduction to Parallel and Distributed Computing
332:571 Virtual Reality Technology
332:573 Data Structures and Algorithms
332:539 Advanced Topics in Digital Signal Processing (consult GD before registering)
332:559 Advanced Topics in Communications (consult GD before registering)
332:579 Advanced Topics in Computer Engineering (consult GD before registering)
332:590 Socially Cognizant Robotics
332:595 Design Methods for Socially Cognizant Robotics
332:640 Robotics and Society
198:520 Introduction to Artificial Intelligence
198:530 Principles of Artificial Intelligence
198:533 Natural Language Processing
198:535 Pattern Recognition: Theory and Applications
198:536 Machine Learning
954:596 Regression and Time Series Analysis for Data Science
960:583 Methods of Inference
960:593 Theory of Statistics
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For students pursuing the MS degree with a specialization in Systems and Controls, the following course requirements apply. All students must take at least 3 Core courses. Those choosing the thesis option must also take a minimum of 3 Restricted Elective courses. On the other hand, students pursuing the non-thesis option are required to take at least 4 Restricted Elective courses. A crucial guideline is that every student must select at least one course from List A of the Restricted Elective courses.
Core Courses
- 332:501 System Analysis
- 332:505 Control System Theory
- 332:506 Applied Controls
- 332:508 Digital Control Systems
- 332:512 Nonlinear Adaptive Control and Learning For Engineers
- 650:504 Advanced Controls I (equivalent to 332:505; credit given for only one)
Restricted Elective Courses: List A
- 332:510 Optimal Control Systems
- 332:514 Stochastic Control Systems
- 332:515 Reinforcement Learning for Engineers
- 332:541 Stochastic Signals and Systems
- 332:509 Convex Optimization for Engineering Applications
- 650:505 Advanced Controls II
Restricted Elective Courses: List B
- 332:519 Advanced Topics in Systems Engineering
- 332:521 Digital Signal Analytics
- 332:530 Introduction to Deep Learning
- 332:532 Probabilistic Methods for Large Scale Signal Processing and Learning
- 332:533 Machine Learning for Inverse Problems
- 332:542 Information Theory and Coding
- 332:543 Communication Networks I
- 332:549 Detection & Estimation Theory: Inference & Machine Learning for Engineers
- 332:539 Advanced Topics in Digital Signal Processing (consult GD before registering)
- 640:507 Functional Analysis I
- 640:515 Ordinary Differential Equations
- 640:517 Partial Differential Equations I
- 640:532 Introduction to Differential Geometry
- 650:512 Robotics and Mechatronics
- 650:606 Advanced Mechanical Engineering Topics: Drones I, II
- 711:557 Dynamic Programming
- 711:652 Nonlinear Optimization
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For students pursuing the MS degree with a specialization in Computer Engineering, the following course requirements apply. All students are required to take a minimum of 3 Core courses. Among these, at least 2 courses should be selected from List A of Core courses, and at least 1 course must be chosen from List B of Core courses. Those choosing the thesis option must also take a minimum of 3 Restricted Elective courses. On the other hand, students pursuing the non-thesis option are required to take at least 4 Restricted Elective courses.
Core Courses: List A
- 332:516 Cloud Computing and Big Data
- 332:518 Mobile Embedded Systems and On-Device AI
- 332:563 Computer Architecture I
- 332:564 Computer Architecture II
- 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 Algorithms
Core Courses: List B
- 332:501 System Analysis
- 332:509 Convex Optimization for Engineering Applications
- 198:510 Numerical Analysis
- 198:521 Linear Programming
- 198:522 Network and Combinatorial Optimization Algorithms
- 198:524 Nonlinear Programming Algorithms
- 642:550 Linear Algebra and Applications
- 642:581 Graph Theory
- 643:621 Mathematical Finance I
- 643:622 Mathematical Finance II
- 643:573 Numerical Analysis I
Restricted Elective Courses
- 332:507 Security Engineering
- 332:515 Reinforcement Learning for Engineers
- 332:530 Introduction to Deep Learning
- 332:539 Advanced Topics in Digital Signal Processing (consult GD before registering)
- 332:543 Communication Networks I
- 332:544 Communication Networks II
- 322:561 Machine Vision
- 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 Technology
- 332:574 Computer Aided Digital VLSI Design
- 332:576 Testing of ULSI Circuits
- 332:577 Analog and Low-Power Digital VLSI Design
- 332:578 Deep Submicron VLSI Design
- 332:579 Advanced Topics in Computer Engineering
- 198:515 Programming Languages and Compilers
- 198:518 Operating Systems Design
- 198:519 Operating Systems Theory
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For students pursuing the MS degree with a specialization in Software Engineering, the following course requirements apply. All students are required to take a minimum of 3 Core courses. Students opting for the thesis option must take at least 3 Restricted Elective courses, with no more than two (2) from List B of Restricted Elective courses. Conversely, those selecting the non-thesis option need to enroll in a minimum of 5 Restricted Elective courses, with a maximum of three (3) from List B of Restricted Elective courses.
Furthermore, if a student's undergraduate transcripts lack any of the following courses, they must be completed:
- 332:252 Programming Methodology I
- 332:351 Programming Methodology II
Enrollment in these courses requires the graduate director’s approval. Please note, these remedial courses do not contribute to the ECE residency requirement.
Core Courses
- 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
- 332:573 Data Structures and Algorithms
- 198:512 Intro. to Data Structures & Alg. (equivalent to 332:573; credit given for only one)
Restricted Elective Courses: List A
- 332:503 Programming Finance
- 332:507 Security Engineering
- 332:516 Cloud Computing and Big Data
- 332:518 Mobile Embedded Systems and On-Device AI
- 332:530 Introduction to Deep Learning
- 332:543 Communication Networks I
- 332:544 Communication Networks II
- 322:561 Machine Vision
- 332:564 Computer Architecture II
- 332:571 Virtual Reality Technology
- 332:579 Advanced Topics in Computer Engineering (consult GD before registering)
Restricted Elective Courses: List B
- 137:541 Enterprise Software Architecture (in-person section only)
- 137:560 Fundamentals of Systems Engg. for Engg. Management (in-person section only)
- 198:508 Formal Languages and Automata
- 198:515 Programming Languages and Compilers I
- 198:518 Operating Systems Design
- 198:519 Operating Systems Theory
- 198:530 Principles of Artificial Intelligence
- 198:543 Massive Data Storage and Retrieval
- 198:544 Computer Security
- 198:546 Computer System Security
- 198:553 Design of Internet Services
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Students pursuing the MS degree with a specialization in Electronic Devices, Circuits and Systems are required to take at least 3 Core courses and 3 Restricted Elective courses from the subsequent lists.
Core Courses
- 332:574 Computer Aided Digital VLSI Design
- 332:578 Deep Submicron VLSI Design
- 332:580 Electric Waves and Radiation
- 332:581 Introduction to Solid State Electronics
- 332:583 Semiconductor Devices I
- 332:587 Transistor Circuit Design
- 332:589 RF Integrated Circuit Design
- 332:599 Advanced Topics in Solid State Electronics: Semiconductors for AI
- 332:599 Advanced Topics in Solid State Electronics: Microelectronic Processing
- 332:599 Advanced Topics in Solid State Electronics: Biosensing and Bioelectronics
Restricted Elective Courses
- 332:557 Quantum Computing and Communication Algorithms
- 332:558 Quantum Computing and Information Systems
- 332:589 RF Integrated Circuit Design
- 332:591 Opto-Electronics I
- 332:592 Opto-Electronics II
- 332:594 Solar Cells
- 332:598 Biomedical Technologies: Design and Development
- 332:599 Advanced Topics in Solid-State Electronics
- 635:503 Theory of Solid-State Materials
- 642:527 Methods of Applied Mathematics I
- 642:528 Methods of Applied Mathematics II
- 750:501 Quantum Mechanics I
- 750:601 Solid State Physics I