Ms in AI
AI and Machine Learning
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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Ready to dive into the world of AI?
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 Courses – 10 credits
- GRAD 50200 – Interdisciplinary AI Fundamentals: Bridging Knowledge (1 credits)
- SCLA 52200 – Artificial Intelligence Policy, Governance, And Ethics (3 credits)
- SCLA 52100 – Societal Impacts of Artificial Intelligence (3 credits)
- GRAD 58900 – Master of Science in Artificial Intelligence Capstone (3 credits)
Required Major Courses – 2 credits
Selective Major Courses – 6 credits
- Selective Major Topic 1: Artificial Intelligence/ML
- Selective Major Topic 2: Data Mining
Technical/Professional Electives – 9 credits
- Students choose a minimum of 9 credit hours of elective courses.
Additional Electives – 3 credits
Any Graduate Level Course with PUO staff advisor approval
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)
- 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) (3 credits)
- ECE 59500 – Selected Topics in Electrical Engineering (Robotics classes) (3 credits)
- ECE 59500 – Selected Topics in Electrical Engineering (Theory & Algorithms) (3 credits)
- ECE 69500 – Advanced Topics in Electrical and Computer Engineering (Optimization for Deep Learning) (3 credits)
- ECON 57600 – Statistical & Machine Learning (2 credits)
- ECON 57800 – Statistical and Machine Learning II (2 credits)
- ME 69700 – Advanced Topics in Scientific Machine Learning (3 CR)
- MGMT 59000 – Directed Readings in Management (Deep Learning) (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 69500 – Epidemic Process Over Networks (1 credit)
- ECE 69500 – Epidemic Processes (1 credit)
- ECE 69500 – Intro to Mathematical Fundamentals for Systems & Control (1 credit)
- MA 59800 – Linear Algebra for Data Science (3 credits)
- MGMT 57100 – Data Mining (2 credits)
Students choose a minimum of 9 credit hours from the Technical/Professional elective list. Students may also select courses from the Machine Learning Selective Topic 1 or 2 lists if not previously completed.
- ASM 54000 – Geographic Information System (GIS) Application (3 credits)
- CNIT 55200 PME – IT Project Management (3 credits)
- CNIT 58100 PRM – Risk Management (1 credit)
- CNIT 58500 PCM – 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)
- EDPS 53100 – Introduction to Measurement and Instrument Design (3 credits)
- ENGT 50700 – Fundamentals of Collaborative Leadership and Agile Strategy (3 credits)
- IT 57100 – Project Management in Business and Industry (3 credits)
- MA 59800 – Linear Algebra for Data Science (1 credit)
- ME 53900 – Introduction to Machine Learning (3 credits)
- MGMT 56800 – Supply Chain Analytics (2 credits)
- MGMT 69000 – Change Management (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)
- 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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