Artificial Intelligence and Machine Learning Minor
Build the Future with AI at Clarkson University
Artificial intelligence and machine learning are transforming nearly every industry, from healthcare and robotics to business, cybersecurity, manufacturing and sustainability. Clarkson University’s new undergraduate minor in Artificial Intelligence & Machine Learning gives students the technical skills and hands-on experience to work with cutting-edge AI technologies and apply them to real-world challenges.
Designed for students from a variety of academic backgrounds, this interdisciplinary minor helps you learn how intelligent systems work, how machines learn from data and how AI can be used to solve complex problems across industries.
Whether you’re interested in engineering, computer science, business, mathematics, healthcare or data-driven innovation, this program prepares you to thrive in a rapidly evolving workforce.
Who Is This Minor For?
The Applied Machine Learning & Artificial Intelligence minor is intentionally designed to be accessible to students across multiple majors and disciplines.
This minor is a great fit for students interested in:
Artificial intelligence and emerging technologies
Coding and problem solving
Data and analytics
Robotics and automation
Engineering innovation
Software development
Research and applied technology
Students can tailor the program to align with their academic interests and career goals through flexible course pathways and electives. No matter your major, learning AI and machine learning skills can help you stand out in today’s technology-driven job market.
A Flexible, Interdisciplinary Curriculum
The minor includes foundational coursework in:
Probability and statistics
Linear algebra
Programming
Machine learning
Students then choose electives that connect AI and machine learning to their interests and career goals.
Elective topics include:
Deep learning
Artificial intelligence
Computer vision
Mobile robotics
AI in manufacturing
Biometrics
Ethics in computing and data science
High-performance computing
Pattern recognition and machine intelligence
This flexible structure allows students to apply AI concepts directly within their own discipline. View our full curriculum below.
Prerequisites are in parentheses next to each course
Required courses (4):
Mathematics: Probability and Statistics (choose one): STAT 389: Probability and Statistics with Multivariate Analysis (MA230 or MA231) STAT 383: Probability and Statistics (MA132) MA 330: Advanced Engineering Mathematics (MA231 and MA232) MA/STAT 381: Probability (MA231 or MA230 (MA211 Recommended))
Mathematics: Linear Algebra (choose one): MA 339: Applied Linear Algebra (MA132; MA230/231 recommended) MA 330: Advanced Engineering Mathematics (MA231 and MA232)
Programming (choose one): CS141: Introduction to Computer Science I IS237: Introduction to Application Development MA/DS 241: Introduction to Data Science (Corequisite: STAT282, or STAT383, or STAT318, or STAT389)
Machine learning (choose one): EE 428: Hands-on introduction to Machine Learning (EE262 OR Instructor approval) IS 400: Applied Machine Learning (IS237 or CS141 or EE261) CS 449: Computational Learning (CS344 and CS345, or consent of the instructor)
Electives (choose two):
STAT 385: Bayesian Data Analysis (STAT 383 or MA/STAT 381, or instructor consent) DS 392: Ethics in Data Science and Applied Mathematics (Any STAT or DS course) EE 402: Machine learning on biomedical signals (MA132, EE321, and BR400 or equivalent or instructor approval) EE 423: Introduction to Biometrics ME 419: Data Science Tools (ME/AE401) CS 451: Artificial Intelligence (CS344 (CS250 and CS341 recommended)) EE 404/CS 470: Deep Learning (CS142, EE262, or EE361, and MA339) EM 450: AI in Manufacturing (OM331, STAT282 or STAT383, and IS237 or IS400) EE 455: Introduction to Mobile Robotics (EE321, EE/ME324, or MA339; or instructor permission) EE 473/CS 473: Computer Vision (CS142 or EE262, and MA339) CS 475: Computing, Ethics, and Society EE 519: High Performance Computing (EE262 or CS142, or consent of instructor) EE 520: Data-driven analysis of complex systems EE 525: Data Analytics for Power System Application (EE333 or Equivalent) EE 526: Detection and Estimation Theory (EE529 or equivalent, or instructor approval) EE 574: Pattern Recognition and Machine Intelligence (MA/STAT383 or MA/STAT381 or EE529 or equivalent) EE 628: Adaptive Signal Processing (EE401/501 or equivalent, and EE529 or equivalent, or instructor approval)
Notes on electives: Students select three electives if MA 330 is used to satisfy both Mathematics requirements. Students cannot get credit for both DS 392 and CS 475 towards the minor. At least two courses applied toward the minor must not overlap with the student’s major requirements (not counting electives).
Clarkson faculty are advancing research and innovation in artificial intelligence, machine learning, robotics, computational modeling and data science. Students benefit from close faculty mentorship, collaborative learning environments and opportunities for undergraduate research and hands-on project experience.
The university’s approach to AI education emphasizes both technical expertise and ethical responsibility, preparing graduates to create technology that makes a meaningful impact.
Clarkson has a long history of preparing students to become innovators, problem-solvers, and leaders in technology-focused fields, going beyond learning theory. The Artificial Intelligence & Machine Learning minor builds on Clarkson’s strengths in engineering, computing, analytics and experiential education. Here, you'll apply AI and machine learning to real-world projects, emerging technologies and industry-focused challenges.
Students graduate with in-demand technical skills, practical experience applying AI tools, interdisciplinary problem-solving abilities and career-ready knowledge for a changing workforce. As AI continues to reshape the future, Clarkson students will be prepared to lead it.
The minor combines coursework in:
Machine learning
Artificial intelligence
Data analysis
Linear algebra and statistics
Deep learning
Computer vision
Robotics
Ethics in AI
Data-driven decision making
Students also gain experience using AI tools and computational methods that are shaping the future of technology and innovation. Clarkson’s strong emphasis on experiential learning means students can explore AI applications in areas like:
Biomedical engineering
Manufacturing
Power systems
Robotics
Data science
Biometrics
Mobile systems
Advanced computing
Career Opportunities in AI & Machine Learning
Artificial intelligence is one of the fastest-growing fields in the world. Employers across industries are seeking graduates with machine learning and AI experience. Students pursuing this minor can prepare for careers in:
Artificial intelligence
Machine learning engineering
Data science
Software engineering
Robotics
Cybersecurity
Healthcare technology
Manufacturing systems
Research and development
Business analytics
The program also provides a strong foundation for graduate study and advanced research in AI-related fields.
Ready to add AI and machine learning expertise to your degree? The Artificial Intelligence & Machine Learning minor helps students build the skills needed to innovate across industries and shape the future of technology.
Contact us to learn more about the program, curriculum and how to add the minor to your academic plan.
