MS in AI
Courses & Faculty
Both majors in the Master of Science in Artificial Intelligence require 30 credit hours to complete. In addition to several required courses, students can customize their plan of study around topics that interest them and are applicable to their career goals. All courses are taught by expert faculty from Purdue’s flagship campus in West Lafayette who have extensive experience in AI and its applications.
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.
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AI Management and Policy Major Courses – 30 credits
Required Courses – 10 credits
- GRAD 50200 – Interdisciplinary AI Fundamentals: Bridging Knowledge (1 credits)
- SCLA 52200 – Artificial Intelligence Policy, Governance, And Ethics (3 credit)
- 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
- GRAD 50300 – AI Essentials: A Non-Technical Introduction (2 credits)
Selective Major Courses – 6 credits
- Selective Major Topic 1: Applications of AI in Business, Non-Profit, Public Sectors
- Selective Major Topic 2: Data Management, Analysis, Communication
Electives – 9 credits (sub header)
- Choose any from the Technical/Professional Electives List
Free Electives – 3 credits (sub header)
- Any Graduate Level Course with PUO staff advisor approval
AI and Machine Learning Major Courses – 30 credits
Required Courses – 10 credits
- GRAD 50200 – Interdisciplinary AI Fundamentals: Bridging Knowledge (1 credits)
- SCLA 52200 – Artificial Intelligence Policy, Governance, And Ethics (3 credit)
- 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
- GRAD 50400 – Advanced AI Fundamentals for Technical Professional (2 credits)
Selective Major Courses – 6 credits
- Selective Major Topic 1: Artificial Intelligence/ML
- Selective Major Topic 2: Data Mining
Electives – 9 credits
- Choose any from the Technical/Professional Electives List
Free Electives – 3 credits
- Any Graduate Level Course with PUO staff advisor approval
Required Major Course – 2 Credit Hours
GRAD 50300-AI Essentials: A Non-Technical Introduction (2 credits)
Selective Major Courses – minimum of 6 Credit Hours
Selective Major 1: Applications of AI in Business, Non-Profit, Public Sectors. Students will choose at least one course from the following:
- MGMT 52500 – Marketing Analytics (2 credits)
- MGMT 68300 – Technology-Driven Business (2 credits)
- POL 52601 – Technology, AI, and Ethics in Policy and Public Administration (3 credits)
- POL 52701 – Local to Global Governance of Data, AI, and Emerging Technology (3 credits)
Selective Major 2: Data Management, Analysis, Communication. Students will choose at least one course from the following:
- ABE 65100 – Environmental Informatics (3 credits)
- CGT 57500 – Data Visualization Tools and Applications (3 credits)
- CNIT 51000 – Data Literacy (3 credits)
- CNIT 57000 BDA – IT Data Analytics (3 credits)
- CNIT 57500 – Data Analysis (3 credits)
- COM 65000 – Communication and Leadership (3 credits)
- EDPS 55600 – Introduction to Quantitative Data Analysis Methods in Education (3 credits)
- EDPS 55700 – Introduction to Quantitative Data Analysis Methods in Education II (3 credits)
- ILS 69500 – Computational Text Analysis (3 credits)
- MGMT 58600 – Python Programming (Python for Analytics) (2 credits)
- MGMT 59000 – Directed Readings in Management (Big Data Analytics in the Cloud) (2 credits)
- MGMT 59000 – Directed Readings in Management (Database and SQL) (2 credits)
- MGMT 59000 – Directed Readings in Management (Visualization and Persuasion) (2 credits)
- MGMT 59000 – Directed Readings in Management (Web Data Analytics) (2 credits)
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: Theory & Algorithms; Robotics classes) (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 59000 – Problems in Economics (Machine Learning II) (2 credits)
- ME 69700 – Advanced Topics in Scientific Machine Learning (3 credits)
- 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 credit)
- 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 and Control (1 credit)
- MA 59800 – Linear Algebra for Data Science (3 credits)
- MGMT 57100 – Data Mining (2 credits)
Technical/Professional Electives.
Students choose a minimum of 9 credit hours from the following list:
- 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)
Free Electives
- Students may choose 3 credit hours if their PUO staff advisor approves or select a course from one of the above lists.