Robotics Engineering Certificate

Three students work on the interior of a medium-sized wheeled robot.

Advance your understanding of autonomous systems with a graduate certificate in Robotics Engineering from Michigan Tech. This graduate certificate focuses on self-teaching, constantly evolving systems that require an advanced understanding of robotics, mathematics, and engineering. Prepare yourself for a leadership role in the robotics industry by delving deeper into the fundamental principles of robotics engineering.

Course Work Summary

Combine theoretical modules with hands-on projects that illustrate real-world applications of your learning. Build a customized certificate program, developing skills in artificial intelligence application, signal processing, cybersecurity, and more.

To enroll, students are required to have a bachelor's degree in robotics engineering, mechatronics, mechanical engineering, or a closely related technical field.

Current Students

Required Courses

Choose at least one course from the list below (up to 9 credits):

EE 5531 - Introduction to Robotics

Introduction to autonomous systems and robotics with focus on automated ground vehicles. Project based course using distributed computing to solve problems related to motion planning, perception, and localization. Requires experience with Linux operating systems variants, version control systems, and C++ or Python.

Credits: 3.0
Lec-Rec-Lab: (2-0-3)
Semesters Offered: Fall, Spring
Restrictions: Permission of department required; Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following College(s): College of Engineering

EE 5532 - Sensing and Processing for Robotics

Sensing modes, signal and image processing for industrial robotic automation processes. Emphasis placed on widely used sensors, including cameras and 3-D sensors for process control and computer vision for autonomous navigation.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s): EE 5522

EE 5615 - AI Engineering Applications

This course introduces practical AI/ML tools for solving real engineering problems. Students learn end-to-end workflows and basic deployment using modern Python libraries. Core topics include supervised learning, unsupervised learning, and reinforcement learning. Application domains emphasize common engineering contexts.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): EE 3180

Elective Courses

Choose up to 6 credits from the following courses, with a limit of 3 credits at the 4000 level:

EE 4253 - Real Time Signal Processing

Practical implementation of digital signal processing concepts as developed in EE4252. Emphasis on applications of DSP to communications, filter design, speech processing, and radar. Laboratory provides practical experience in the design and implementation of DSP solutions.

Credits: 3.0
Lec-Rec-Lab: (2-0-2)
Semesters Offered: Spring
Pre-Requisite(s): EE 4252

EE 4615 - AI Engineering Applications

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Pre-Requisite(s): EE 3180

EE 5522 - Digital Image Processing

Fundamentals of image processing are covered including image representation, geometric transformations, binary image processing, compression, space and frequency domain processing. Computer programming in MATLAB and Python required.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall, Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical & Computer Engineer, Electrical Engineering, Computer Engineering

EE 5533 - Neuromorphic Robotic Systems

The course offers an interdisciplinary perspective on neuromorphic computing including neuroscience, algorithms, hardware implementations and applications. Through this course, the students will learn how build computational models of brain and use neuromorphic system to control robots.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall, Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following Major(s): Electrical Engineering, Electrical & Computer Engineer

EE 5841 - Machine Learning

This course will explore the foundational techniques of machine learning. Topics are pulled from the areas of unsupervised and supervised learning. Specific methods covered include naive Bayes, decision trees, support vector machine (SVMs), ensemble, and clustering methods.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior

CS 5740 - Development of Trusted Software

This course exposes students to the concepts of secure software development. Students will learn how to develop high-quality software that is resistant against cyber- attacks, by minimizing the number of vulnerabilities that can be exploited by an attacker. Topics include: access control, race conditions, buffer overflows, code injection, fuzzing techniques, cryptographic software, web application and Java security.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Pre-Requisite(s): CS 4471

CS 5821 - Computational Intelligence - Theory and Application

This course covers the four main paradigms of Computational Intelligence, viz., fuzzy systems, artificial neural networks, evolutionary computing, and swarm intelligence, and their integration to develop hybrid systems. Applications of Computational Intelligence include classification, regression, clustering, controls, robotics, etc.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: On Demand
Restrictions: Permission of instructor required; Must be enrolled in one of the following Level(s): Graduate

CS 5841 - Deep Learning

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Permission of instructor required; May not be enrolled in one of the following Class(es): Freshman, Sophomore, Junior
Pre-Requisite(s): CS 4801

CS 5861 - Artificial Intelligence Theory and Applications

Fundamental ideas and techniques used in the construction of problem solvers that use Artificial Intelligence technology. Topics include knowledge representation and reasoning, problem solving, heuristics, search heuristics, inference mechanisms, and machine learning.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate
Pre-Requisite(s): CS 2311 and CS 2321

ME 5190 - Machine Learning for Engineering Applications

This course introduces students to machine learning algorithms and their applications in engineering. Topics include supervised and unsupervised learning algorithms. Students will apply machine learning techniques to problems in different areas including mechanical, biomedical, and materials design problems.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: Must be enrolled in one of the following Level(s): Graduate; Must be enrolled in one of the following College(s): College of Engineering

HF 4880 - Usability Assessment

Explore the concept of usability and how this is assessed and applied to various products, interfaces, systems, and information with a focus on heuristic evaluation, cognitive walkthroughs, card sorting, tree testing, surveys, interviews, and ISO standards.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: On Demand

The following classes may also be available as electives, with course numbers supbject to change:

EE 5XXX: Advanced Robotics (3 credits)
- Prerequisite: EE 3261 (or ME 4775 or equivalent)
EE 5XXX: Machine Learning for Robotics (3 credits)
- Prerequisite: EE 4235 or EE 5522 (or equivalent)
EE 5XXX900: Rehabilitation Robotics (3 credits)
- Prerequisite: EE 3261 (or ME 4775 or equivalent)
EE 5XXX: Cyber-physical system cybersecurity (3 credits)
EE 5XXX: Probabilistic Graphic Models (SLAM)

Application Process and Admissions Requirements

Applications are reviewed on an individual basis using a holistic approach. Fill out our free graduate application online to apply to any of our programs. Official transcripts and scores are not required for the initial application, although you will need to upload them later.

Graduate School Admissions Process

Applying to the Graduate School is free (no application fees) and fast (no official transcripts, test scores, or letters needed to start). The application process involves three easy steps.

See Admissions Steps

Graduate School Requirements

To be considered for admission to the Graduate School as a degree- or certificate-seeking student, you need to:

have a bachelor's degree or its equivalent from an accredited institution, and
be prepared for advanced study in your chosen field, as demonstrated by your previous degree and your scholastic record.

See additional application requirements, including required materials:

Student Statements

Official Transcripts

Program Specific Requirements

Program Specific

Résumé / Curriculum vitae
Admitted applicants typically have an undergraduate GPA of 3.0 or better on a 4.0 scale
GMAT/GRE: Not required
Preferred major: Electrical Engineering or closely related technical field - Students with degrees in other fields may be required to take preparatory courses

International Students

TOEFL scores as of January 21, 2026: Recommended Score of 4.5
- For tests taken before January 21, 2026: Recommended Score of 79 iBT
IELTS: Recommended Overall Band Score of 6.5

Admissions Decisions

Made on a rolling basis.

Certificate Admissions

Prospective Students

Prospective students and non-degree seeking students interested in obtaining a graduate certificate must apply to the certificate program using the online application.
Accelerated certificate options exist for current Michigan Tech undergraduate students in good standing and have achieved at least junior-level standing or recent bachelor’s graduates of Michigan Tech (less than two years).
The number of credits earned prior to admission that can be counted are restricted.

Current Graduate Students

Current Michigan Tech graduate students who are in a degree program do not need to formally apply. Current students must still submit the appropriate degree schedule in order to complete the certificate.

International Student Requirements

International Students must apply and be accepted into a degree-granting program in order to earn a graduate certificate. A non-refundable $10 processing fee per application is required.

See International Applicants