AI and Machine Learning

Ms in AI

The AI and Machine Learning major is one of two specialized majors in Purdue University’s 100% online Master of Science in Artificial Intelligence program. This major equips you with advanced expertise in programming, computer science, and mathematics, empowering you to drive innovation in one of the most rapidly evolving fields today. Graduates of this major are primed for exciting, high-impact careers at the forefront of AI, technology, and engineering, where they will tackle real-world challenges and shape the future of industries ranging from healthcare to robotics. Explore the curriculum, career opportunities, and the resources Purdue University offers to propel your career to new heights.

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Career Outlook

Get your career started by working in one of the following roles: Data Scientist, Software Developer, Machine Learning Engineer, Natural Language Processing Engineer, Data Analyst, Robotics Engineer, Computer Vision Engineer, UX/UI Designer, Network Architect, Systems Analyst and more!

Purdue University’s rigorous online programs allow you to earn a prestigious Purdue degree anytime and from anywhere. These programs give you access to outstanding faculty and top-quality curriculum in a convenient, flexible format to move your career and the world forward.

Curriculum Overview

Build In-Demand AI Skills Through Hands-On Learning

Prepare yourself for a career in technology or learn to incorporate AI skills into your current role.
Build technical skills in AI by completing hands-on projects with real-world applications
Gain expertise in in-demand areas like machine learning, programming, data engineering, project management and more.

The AI and Machine Learning major requires a total of 30 credit hours. A detailed breakdown of the curriculum can be found below. If you are looking for more information on courses, please check out the Purdue University Course Catalog. Enter the course code into the search bar including the prefix and number to find the specific course in the catalog.

Required Major Course – minimum of 2 Credit Hours

GRAD 50400-Advanced AI Fundamentals for Technical Professional (2 credits)

Selective Major Courses – minimum of 6 Credit Hours

Selective Major 1: Artificial Intelligence/ML. Students will choose at least one course from the following:

CNIT 58100 – Natural Language Processing (3 credits)
CNIT 57300 – Adversarial Techniques in AI (3 credits)
CNIT 57400 – Information Security and Privacy in LLMs (3 credits)
CS 57800 – Statistical Machine Learning (3 credits)
ECE 50024 – Machine Learning (3 credits)
ECE 57000 – Artificial Intelligence (3 credits)
ECE 59500 – Selected Topics in Electrical Engineering (Reinforcement Learning: Theory and Algorithms) (3 credits)
ECE 59500 – Selected Topics in Electrical Engineering (Computer Vision for Embedded Systems) (1 credit)
ECE 50874 – Advanced Software Engineering (3 credits)
ECE 69500 – Advanced Topics in Electrical and Computer Engineering (Optimization for Deep Learning) (3 credits)
ECE 69500 – Machine Learning for Bioinformatics and Healthcare (3 credits)
ECE 56900 – Introduction to Robotic Systems (3 credits)
ECON 57600 – Statistical & Machine Learning (2 credits)
ECON 57800 – Statistical and Machine Learning II (2 credits)
GRAD 50900 – Applied Machine Learning: From Foundations to Latest Advances (3 credits)
ME 69700 – Advanced Topics in Scientific Machine Learning (3 credits)
ME 59700 – AI in Thermal Systems (3 credits)
MIS 57600- Deep Learning for Business Analytics (2 credits)

Selective Major 2: Data Mining. Students will choose at least one course from the following:

CS 50023 – Data Engineering I (1 credit)
CS 50024 – Data Engineering II (1 credit)
CS 50025 – Foundations of Decision Making (1 credit)
CS 57300 – Data Mining (3 credits)
CS 59000 – Topics in Computer Sciences (Foundations in Computer Science) (1 credit)
CS 59000 – Topics in Computer Sciences (Numerical Computing for Data Science) (1 credits)
ECE 50836 – Intro to Data Mining (3 credits)
ECE 59500 – Data Analysis, Design of Experiments, and Machine Learning (1 credit)
ECE 60283 – Networked Epidemic Processes (1 credit)
ECE 60282 – Epidemic Processes (1 credit)
ECE 60281 – Intro to Mathematical Fundamentals for Systems & Control (1 credit)
GRAD 50800 – Data Analytics (3 credits)
ILS 65100 – Introduction to Natural Language Processing (1 credit)
MA 51100 – Linear Algebra with Applications (3 credits)
QM 57100 – Data Mining (2 credits)

Students complete 9 credits from Technical/Professional electives, AI and Machine Learning Selective Major Topics 1 or 2, or AI Management and Policy courses not previously taken.

ASM 54000 – Geographic Information System (GIS) Application (3 credits)
CNIT 55200 – IT Project Management (3 credits)
CNIT 57200 – AI Applications in Cybersecurity
CNIT 58100 – Risk Management (1 credit)
CNIT 58500 – Organizational and Change Management for IT Projects (3 credits)
COM 60311 – Seminar in Crisis Communication (3 credits)
ECE 56900 – Introduction to Robotic Systems (3 credits)
ECE 59500 – Selected Topics in Electrical Engineering (Computer Vision for Embedded Systems) (1 credit)
ECE 59500 – Advanced Software Engineering (3 credits)
ECE 60864 – Advanced IoT Design & Applications (3 credits)
EDPS 53100 – Introduction to Measurement and Instrument Design (3 credits)
ENGT 50700 – Fundamentals of Collaborative Leadership and Agile Strategy (3 credits)
GRAD 50600 – Big Data Tools and Technologies (3 credits)
ME 53900 – Introduction to Machine Learning (3 credits)
SCOM 56800 – Supply Chain Analytics (2 credits)
MIS 57600 – Database and SQL (2 credits)
MIS 59300 – Business Data Visualization and Analytics (2 credits)
OLS 57900 – Emerging World-Class Leadership Strategies (3 credits)
OLS 58000 – Interpersonal & Group Skills for Leaders (3 credits)
OLS 58100 – Leading Teams (3 credits)
OLS 58200 – Leadership and Organizational Change (3 credits)
POL 50601 – Data, Machine Learning, and Artificial Intelligence for Public Sector (3 credits)
POL 55101: Governance Auditing of AI: Sociotechnical Framework and Practices (3 credits)
POL 52801: Organizational Governance of Technology of AI (3 credits)
PHRM 50000 – AI and Its Use in Clinical Care (3 credits)
PHIL 50800 – Advanced Ethics of Data Science (3 credits)
PHIL 50000 – Advanced Ethics for Technology, Engineering, and Design (3 credits)
POL 52101 – Applied Public Policy: Institutions, Processes, Practices (3 credits)
POL50100 – Political Science: Methodology (3 credits)
POL 52901 – Applied Policy and Program Evaluation (3 credits)
SCLA 50900 – Strategic Intelligence: Organizations, Technologies, Procedures, and Policies (3 credits)
SCLA 53000 – Strategic Foresight and Leadership for Defense Leaders (3 credits)
STAT 59800 – Topics in Statistical Methods (Probability and Statistics) (1 credit)

Additional Electives

Students may choose 3 credit hours if their PUO staff advisor approves or select a course from one of the above categories.

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Career Outlook

Curriculum Overview

Build In-Demand AI Skills Through Hands-On Learning

Courses Breakdown

Major Courses

Elective Courses

Optional Concentration – AI Technology Policy and Governance

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