M.S. in Robotics Coursework

Course Listing

This Minnesota Robotics Program is designed to be completed over the course of three semesters for a total of 31 credits. One course will be required from each key area (for a total of 9 to 10 credits), selected from a shortlist of foundational courses for each area. The remaining credits will be for the capstone (3 to 6 credits) or thesis credits (10 credits), the Robotics Colloquium (1 credit), with the remaining credits coming from a list of elective courses from participating departments.

You can view more information about each course in the University of Minnesota's M.S. in Robotics program catalog.

Robotics Colloquium (1 credit) — Required

The Robotics Colloquium will be an introductory class in which new students will be confronted with the basics of robotics and sensing while getting an idea of the industry, opportunities, and how the Minnesota Robotics Institute's Master's program can work for them.

Key Areas (9 to 10 credits total) — Choose one (1) from each area.

Cognition
- Computer Science 5511 - Artificial Intelligence I (3 cr)
- Computer Science 5512 - Artificial Intelligence II (3 cr)
- Computer Science 5521 - Intro to Machine Learning (3 cr)
- Computer Science 5525 - Machine Learning: Analysis and Methods (3 cr)
Perception
- Computer Science 5561 - Computer Vision (3 cr)
- Electrical Engineering 5271 - Robot Vision (3 cr)
- Electrical Engineering 5561 - Image Processing and Applications (3 cr)
Robot Modeling and Control:
- Computer Science 5551 - Introduction to Intelligent Robotic Systems (3 cr)
- Computer Science 5552 - Sensing/Estimation in Robotics (3 cr)
- Mechanical Engineering 5286 - Robotics (4 cr, includes a lab)
- Electrical Engineering 5231/Aerospace Engineering and Mechanics - 5321 Linear Systems and Optimal Control/Modern Feedback Control (3 cr)
- Electrical Engineering 8950 - Advanced Topics in Electrical and Computer Engineering: Introduction to Controls and Signals for Robotics (3 cr)

Final Projects — Choose one (1), either Plan A: Master's Thesis OR Plan B: Capstone project

Plan A: Master's Thesis (10 cr): Master’s theses (Plan A) will be supervised by a faculty advisor and follow the University rules. Students will have a committee of at least three faculty, write a thesis, and defend it orally to their committee. The Master’s thesis is especially useful for students who are considering later transferring to a Ph.D. degree or who are interested in a deeper research experience. View the Plan A student planning checklist for definitive grade and credit requirements, as well as milestone and policy procedures for Plan A.
Plan B: Capstone Project (3 - 6 cr): Capstone projects are supervised by faculty and may also be conducted in collaboration with industry partners. A capstone project will require at least one semester (3 cr for the Plan B course) but can be extended to two semesters. Students will document their capstone work in a final report and will present their work in the form of a video presentation to their final exam committee of 3 faculty members. View the Plan B student planning checklist for definitive grade and credit requirements, as well as milestone and policy procedures for Plan B.

Elective Courses (fill in credit total to 31 credits) — Choose up to six (6) additional classes

Students may also use courses from the three key areas as electives, though a course can only count toward the program's core course requirement or the elective requirement, not both.

Aerospace Engineering and Mechanics
- AEM 5333 - Design-to-Flight: Small Uninhabited Aerial Vehicles
- AEM 5451 - Optimal Estimation (Joint with EE 5251)
- AEM 8411 - Advanced Dynamics
- AEM 8421 - Robust Control (joint with EE 5235)
- AEM 8423 - Convex Optimization in Control
- AEM 8495 - Advanced Topics Aerospace Systems (Topics course)
Biomedical Engineering
- BMEN 5151 - Introduction to BioMEMS and Medical Microdevices
Computer Science
- CSCI 5211 - Data Communications and Computer Networks
- CSCI 5125 - Collaborative and Social Computing
- CSCI 5231 - Wireless and Sensor Networks
- CSCI 5451 - Introduction to Parallel Computing
- CSCI 5511 - Artificial Intelligence I
- CSCI 5512 - Artificial Intelligence II
- CSCI 5521 - Intro to Machine Learning
- CSCI 5523 - Introduction to Data Mining
- CSCI 5525 - Machine Learning
- CSCI 5527 - Deep Learning: Models, Computation and Applications
- CSCI 5541 - Natural Language Processing
- CSCI 5551 - Introduction to Intelligent Robotic Systems
- CSCI 5552 - Sensing/Estimation in Robotics
- CSCI 5561 - Computer Vision
- CSCI 5563 - Multiview 3D Geometry in Computer Vision
- CSCI 5607 - Fundamentals of Computer Graphics 1
- CSCI 5609 - Visualization
- CSCI 5619 - Virtual Reality and 3D Interaction
- CSCI 5980 - Special Topics in Computer Science (Topics course)
- CSCI 8581 - Big Data in Astrophysics
- CSCI 8980 - Special Advanced Topics in Computer Science (Topics course)
Design
- DES 5185 - Human Factors in Design
- DES 5901 - Principles of Wearable Technology
- DES 5902 - Wearable Technology Laboratory Practicum
Electrical Engineering
- EE 5231 - Linear Systems and Optimal Control (Joint with AEM 5321)
- EE 5235 - Robust Control System Design (Joint with AEM 8421)
- EE 5239 - Introduction to Nonlinear Optimization
- EE 5241 - Optimal Control and Reinforcement Learning
- EE 5251 - Optimal Filtering and Estimation (Joint with AEM 5451)
- EE 5271 - Robot Vision
- EE 5373 - Data Modeling Using R
- EE 5391 - Computing with Neural Networks
- EE 5505 - Wireless Communication
- EE 5531 - Probability and Stochastic Processes
- EE 5542 - Adaptive Digital Signal Processing
- EE 5561 - Image Processing and Applications
- EE 5621/2 - Physical Optics and Physical Optics Lab
- EE 5624 - Optical Electronics
- EE 5705/7 - Electric Drives in Sustainable Energy Systems and Lab
- EE 5940 - Special Topics in Electrical Engineering I (Topics Course)
- EE 8215 - Nonlinear Systems
- EE 8231 - Optimization Theory
- EE 8581 - Detection and Estimation Theory
- EE 8591 - Predictive Learning from Data
- EE 8950 - Advanced Topics in Electrical and Computer Engineering
Industrial and Systems Engineering
- IE 5080 - Topics in Industrial Engineering
- IE 5561 - Analytics and Data-Driven Decision Making
- IE 8534 - Advanced Topics in Operations Research
Human Factors
- HUMF 5874 - Human Centered Design to Improve Complex Systems
Mechanical Engineering
- ME 5241 - Computer-Aided Engineering
- ME 5243 - Advanced Mechanism Design
- ME 5248 - Vibration Engineering
- ME 5286 - Robotics
- ME 8243 - Topics in Design: Advanced Fluid Power (Topics Course)
- ME 8281 - Advanced Control System Design
- ME 8283 - Design of Mechatronic Products
- ME 8285 - Advanced Control System Design, with Applications to Smart Vehicles
Psychology
- PSY 8036 - Topics in Computer Vision (Topics Course)

Return to Top

Industry and Academic Collaboration

Courses are paused in the summertime to allow students to participate in highly coveted and competitive internships and programs in the industry.
As mentioned under the benefits section, MnRI faculty is connected to key industries and top companies in the state of Minnesota and beyond. We are currently adding on-campus staff from Honeywell, and there are vast research options in the state of Minnesota, which has large local presences from 3M, Amazon, Honeywell, Toro, General Mills, Boston Scientific, Medtronic, Land O'Lakes, and much more.
MnRI enjoys many benefits of connecting to about three dozen faculty from across the University of Minnesota. However, the connections do not stop in the Twin Cities. We entertain almost weekly speakers from across the world to talk about their research and issues within the robotics industry and collaborate with many educational and governmental institutions.