Minor in Artificial Intelligence

Gain a competitive edge in a rapidly evolving world with the Artificial Intelligence minor at Michigan Technological University. This 18-credit program equips you with in-demand skills in machine learning, data-driven problem solving, and AI applications across industries like healthcare, finance, manufacturing, and beyond. You’ll build a strong technical foundation while exploring the ethical and societal impacts of AI, preparing you to design, implement, and evaluate intelligent solutions responsibly. Whether you’re majoring in computing or another field, this minor complements your degree with practical, future-focused expertise that employers are actively seeking.

How to Enroll in a Minor

To pursue a minor, you must first be enrolled in a bachelor's degree program and submit a request to add a minor to your curriculum through the Curriculum Change Portlet in Experience (view the My Degree card and select Request to Change Major, Minor, or Concentration). The minor advisor will receive notification and contact you with any questions before reviewing and approving your request. We recommend that you begin your minor studies as early as possible in your academic career.

Download the artificial intelligence minor audit for complete information.

Please note that the minor in artificial intelligence is not available to BS in Artificial Intelligence majors.

Required Courses

The minor would consist of a minimum of 18 credits.

Computing Fundamentals I

Complete one of the options below. If you select DATA1200, you will need to complete at least one of the other CS courses in this list of progress.

DATA 1200 - Data Science with Python

The course introduces data science topics including inferential and computational thinking using Python. Topics include table operations, functions, control structures, visualization, sampling, and inference

Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Spring
Pre-Requisite(s): MA 1030(C) or MA 1031(C) or MA 1032(C) or MA 1160(C) or MA 1161(C)

CS 1122 - Introduction to Programming II

Continuation of CS 1121. Topics include data abstraction, class hierarchies and polymorphism, list, stack, queue and tree data structures, complexity-based algorithm and data structure choices, and recursion. Homework programming assignments are given.

Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Fall, Spring, Summer
Pre-Requisite(s): CS 1121

CS 1131 - Accelerated Introduction to Programming

An alternative starting point of the computer science programs for students with some programming experience, combining material from CS1121 and CS1122, offered at an accelerated pace. Homework programming assignments are given.

Credits: 5.0
Lec-Rec-Lab: (0-4-2)
Semesters Offered: Fall
Restrictions: Permission of department required
Pre-Requisite(s): MA 1031(C) or MA 1032(C) or MA 1120(C) or MA 1160(C) or MA 1161(C) or MA 1121(C)

Computing Fundamentals II

Complete one (3 credits) of the options below.

CS 2321 - Data Structures

Presents fundamental concepts in data structures. Topics include abstract data types (priority queues, dictionaries and graphs) and their implementations, algorithm analysis, sorting, text processing, and object-oriented design. A significant programming project is assigned.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring
Pre-Requisite(s): CS 1122 or CS 1131

DATA 2201 - Foundations of Data Science

Introduces data science technologies and methods that provide a foundation for subsequent Data Science classes. Topics covered include working with data and applied linear algebra in standard numerical computing libraries.

Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Fall
Pre-Requisite(s): (DATA 1202 or DATA 1200) and ((CS 1122 or CS 1131) or (CS 2311 and CS 2321)) and (MA 2320(C) or MA 2330(C) or MA 2321(C))

Introduction to AI Applications and Tools

Complete one (3 credits) of the options below.

SAT 3903 - AI Tools and Applications

This course provides a hands-on, practical introduction to a wide array of applied AI tools with a focus on direct, practical applications using generative AI for content creation, data analysis, and other areas. Students will learn how to select, use, and evaluate AI tools to solve real-world problems and enhance productivity.

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

AI Ethics

Complete one (3 credits) of the options below.

CS 3000 - Ethical and Social Aspects of Computing

An examination of social and ethical issues associated with computing. Topics include: ethical theories and decision making, intellectual property, freedom of expression, privacy, security, and professional responsibility.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall, Spring
Restrictions: Must be enrolled in one of the following Class(es): Junior, Senior
Pre-Requisite(s): CS 3141

HU 3704 - Ethics of Artificial Intelligence

An introduction to the philosophical issues raised by current and future AI systems with a special focus on ethical foundations and general normative concerns. Including AI prediction, classification, manipulation, surveillance; AI agency, responsibility moral status; the future of work; human rights; use of AI in governance, health, education, and more.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall, Spring

AI and Machine Learning I

Complete one (3 credits) of the options below.

CS 4861 - 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: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s): CS 2311 and CS 2321

CS 4801 - Foundations of Machine Learning

The course covers classical statistical machine learning. Topics include classification (k-nearest neighbors, decision trees, random forests, logistic regression, naive bayes, neural networks), regression, clustering (partitional and hierarchical clustering), and evaluation.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): DATA 2201 and (MA 2710 or MA 2720 or MA 3710 or MA 3720)

AI and Machine Learning II

Complete one (3 credits) of the options below. Can choose between CS3461 or EE3144 or EE4235. Can also choose between EET4501 or MIS4000 or EE4615.

CS 4861 - Artificial Intelligence Theory and Applications

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s): CS 2311 and CS 2321

CS 4801 - Foundations of Machine Learning

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): DATA 2201 and (MA 2710 or MA 2720 or MA 3710 or MA 3720)

CS 3461 - Introduction to Robotics

Hands-on exploration of robot design and programming using real mobile robotic platforms. Robots feature onboard sensing such as cameras and other advanced perception tools. Students learn kinematics, actuation, sensing, configuration spaces, control, and motion planning while developing and testing on physical robots in collaborative teams.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman
Pre-Requisite(s): CS 3421 or EE 3172

EE 4235 - Sensing and Processing in Robotic Applications

Sensing and signal processing for robotics applications in manufacturing and autonomous navigation. Heavy emphasis on developing, testing, and evaluating algorithms. MATLAB programming required.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Fall
Restrictions: Must be enrolled in one of the following Class(es): Junior, Senior
Pre-Requisite(s): EE 2180 and (ENG 1101 or ENG 1101T)

SAT 4520 - Machine Learning in Security

Study of artificial intelligence and machine learning in cybersecurity. Topics include fundamentals of common machine learning and deep learning algorithms, intelligent threat detection and analysis, user behavior analytics, machine learning in hacking, and automated cybersecurity systems.

Credits: 3.0
Lec-Rec-Lab: (0-2-2)
Semesters Offered: Spring
Pre-Requisite(s): SAT 3812 and SAT 4310

SAT 4114 - Artificial Intelligence in Healthcare

This course introduces students to clinical data and artificial intelligence (A1) methods in healthcare. Health AI topics such as risk prediction, medical image analysis, natural language processing of clinical text, computer vision, and the integration of AI, bias in algorithm development, bioethics, and regulation into the clinical environment are covered.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s): SAT 4650

EET 4501 - Applied Machine Learning

Introduces the general concepts and algorithms of machine learning (ML) with their implementation and applications to practical problems of modeling, detection, estimation, prediction, and control. Applications include cybersecurity, healthcare, robot vision, remote sensing, automation, and natural language processing.

Credits: 3.0
Lec-Rec-Lab: (3-0-0)
Semesters Offered: Spring
Restrictions: May not be enrolled in one of the following Class(es): Freshman, Sophomore
Pre-Requisite(s): SAT 4310 or SAT 4650

MIS 4000 - AI and Emerging Technologies for Business

Focuses on understanding AI and IT for competitive advantage and as an agent of transformation. Topics include managing AI, IT infrastructure and architecture, facilitating information distribution throughout the enterprise, business applications for machine learning and artificial intelligence, and other emerging trends and technologies.

Credits: 3.0
Lec-Rec-Lab: (0-3-0)
Semesters Offered: Fall
Pre-Requisite(s): MIS 2100 or CS 1122 or CS 1131

EE 4615 - 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
Pre-Requisite(s): EE 3180