Master of Science in Autonomous Systems and Robotics
This master of science degree program advances knowledge through post-baccalaureate coursework and optional project-based research. The program requires 30 credit hours. Students have the option of completing a thesis based on up to eight credit hours of research (MMAE 591) with the approval of a thesis adviser, or completing the program with courses, which may include up to six credit hours of projects (MMAE 594 or MMAE 597). In line with the department's approach to its graduate programs, a student has considerable flexibility, in consultation with their adviser, in formulating an M.S. program. Registration and 80%-class session attendance--required for a passing grade--in the Seminar course MMAE 593 is required of all M.S. graduate students with thesis and Ph.D. students in each semester of full-time enrollment.
Before completion of the first semester of graduate study, full-time students should select an academic adviser. Graduate students pursuing the M.S. degree on a part-time basis should select an academic adviser before registering for their twelfth credit hour. The student, in consultation with the adviser, prepares a program of study that reflects individual needs and interests. For this program, course plans should be based on an interdisciplinary list of approved courses. When necessary, course substitution is possible upon approval by the graduate study committee. Students with the thesis option are required to pass an oral comprehensive examination on their thesis and related topics. The examination committee consists of at least three appropriate faculty members who are nominated by the thesis adviser.
Admissions Requirements: GPA 3.0, TOEFL – according to university minimum standards and B.S. Degree in Materials Science and Engineering, Mechanical Engineering, Aerospace Engineering, Chemical Engineering, Electrical Engineering, or Physics.
Master of Science in Autonomous Systems and Robotics (Coursework Only Option)
| Minimum Credits Required | 30 |
| Maximum 400-Level Credit | 9 |
| Maximum 700-Level Credit | 6 |
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses | (9) | |
| MMAE 501 | Engineering Analysis I | 3 |
| MMAE 541 | Advanced Dynamics | 3 |
| MMAE 543 | Modern Control Systems | 3 |
| Autonomous Systems and Robotics (ASR) Electives | (21) | |
| Select 21 credit hours from the following: | 21 | |
| Aircraft Flight Mechanics | 3 | |
| Spacecraft Dynamics | 3 | |
| Systems Analysis and Control | 3 | |
| Computer-Aided Design and Manufacturing | 3 | |
| Electrified Vehicle Powertrains | 3 | |
| Data Driven Modeling | 3 | |
| Engineering Analysis II | 3 | |
| Methods and Applications of Deep Learning | 3 | |
| Robotics | 3 | |
| Advanced CAD/CAM | 3 | |
| Optimal Control | 3 | |
| Optimal State Estimation | 3 | |
| Space Environment | 3 | |
| Introduction to Navigation Systems | 3 | |
| Project for Master of Engineering Students | 1-3 | |
| Special Topics | 1-3 | |
| Applied Optimization for Engineers | 3 | |
| Computer Vision and Image Processing | 3 | |
| Machine and Deep Learning | 3 | |
| Statistical Signal Processing | 3 | |
| Cyber-Physical Systems Security and Design | 3 | |
| Machine Learning | 3 | |
| Regression | 3 | |
| Stochastic Partial Differential Equations | 3 | |
| Modern Methods in Discrete Applied Mathematics | 3 | |
| Regression | 3 | |
| Bayesian Computational Statistics | 3 | |
| Total Credit Hours | 30 | |
Master of Science in Autonomous Systems and Robotics (Thesis Option)
| Minimum Credits Required | 30 |
| Maximum 400-Level Credit | 9 |
| Maximum 700-Level Credit | 6 |
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses | (9) | |
| MMAE 501 | Engineering Analysis I | 3 |
| MMAE 541 | Advanced Dynamics | 3 |
| MMAE 543 | Modern Control Systems | 3 |
| Autonomous Systems and Robotics (ASR) Electives | (13-15) | |
| Select 13-15 credit hours from the following: | 13-15 | |
| Aircraft Flight Mechanics | 3 | |
| Spacecraft Dynamics | 3 | |
| Computer-Aided Design and Manufacturing | 3 | |
| Data Driven Modeling | 3 | |
| Engineering Analysis II | 3 | |
| Robotics | 3 | |
| Advanced CAD/CAM | 3 | |
| Optimal Control | 3 | |
| Optimal State Estimation | 3 | |
| Space Environment | 3 | |
| Introduction to Navigation Systems | 3 | |
| Special Topics | 1-3 | |
| Machine Learning | 3 | |
| Applied Optimization for Engineers | 3 | |
| Cyber-Physical Systems Security and Design | 3 | |
| Computer Vision and Image Processing | 3 | |
| Machine and Deep Learning | 3 | |
| Statistical Signal Processing | 3 | |
| Regression | 3 | |
| Regression | 3 | |
| Statistical Learning | 3 | |
| Bayesian Computational Statistics | 3 | |
| Thesis Research | (6-8) | |
| MMAE 591 | Research and Thesis M.S. | 6-8 |
