Program Overview

Master of Science in Artificial Intelligence and Innovation

The Master of Science in Artificial Intelligence and Innovation (MSAII) program combines a rigorous AI and machine learning curriculum with real-world team experience in innovation and entrepreneurship.

Overview

The Master of Science in Artificial Intelligence and Innovation (MSAII) program is a successor to the M.S. in Biotechnology, Innovation and Computing (MSBIC). It combines a rigorous AI and machine learning curriculum with real-world team experience in identifying an AI market niche and developing a responsive product in cooperation with external stakeholders. The core program, which lasts four semesters and leads to a capstone project, focuses on both intrapreneurship and entrepreneurship, equipping graduates to either begin a startup or develop a new organization within an existing company. Students will also gain critical practical skills, such as making persuasive technical presentations, assembling development teams, and evaluating the potential of new market ideas.

Requirements

Incoming students generally hold undergraduate degrees in computer science, software engineering, bioinformatics or bioengineering. To earn the MSAII degree, you must pass courses in the Core Curriculum, the Knowledge Requirements and Electives. You must also complete a capstone project in which you work on a development project as part of the Core Curriculum. In total, you will complete 192 eligible units of study, including 84 units of Core Curriculum (including the 36-unit Capstone), 72 units of Knowledge Requirements and at least 36 units of approved Electives.

Curriculum

To earn the MSAII degree, you must pass courses in the Core Curriculum, the Knowledge Requirements and Electives. You must also complete a capstone project in which you work on a development project as part of the Core Curriculum. In total, you will complete 195 eligible units of study, including 84 units of Core Curriculum (including the 36-unit Capstone), 72 units of Knowledge Requirements, at least 36 units of approved Electives and the LTI Practicum (3 units, associated with your summer internship). The purpose of the Core Curriculum is to prepare you to discover new AI applicants and develop them into a product suitable for further development, often leading to a startup enterprise.

Preparation Prerequisite

Historically, students typically need a refresher on basic computer science systems before beginning graduate work at CMU. You must pass the undergraduate course 15-513 Introduction to Computer Systems (6 units), typically in the summer before your program commences.

Curriculum Components

Each major has different core curriculum requirements.

• Core Curriculum (84 units)
  • This is a five-course sequence based on the four main phases of innovation development, including opportunity identification, opportunity development, business planning and incubation of a business with a viable product. The courses must be taken in the order listed:
    1. 11-651, Artificial Intelligence and Future Markets (12 units). First fall semester.
    2. 17-762, Law of Computer Technology (12 units). First fall semester.
    3. 11-695, AI Engineering (12 units). First spring semester.
    4. 11-654, AI Innovation (12 units). Second fall semester.
    5. 11-699, Capstone Project (36 units). Second spring semester.
• Knowledge Requirements (72 units)
  • This is a set of six rigorous courses to ensure that you are able to develop advanced AI applications.
    1. 11-601, Coding Bootcamp (12 units). First fall semester.
    2. 10-601, Machine Learning (12 units), First fall semester.
    3. 11-785, Deep Learning (12 units), First Spring semester.
    4. 11-611, Natural Language Processing (12 units). Second fall semester.
    5. 10-623 Generative AI (12 units). First spring semester. OR 11-667, Large Language Models (12 units). Second fall semester.
    6. A 12-unit course in AI, NLP, or ML. Second fall semester.

Internship

Every student is required to complete an industry internship during the summer between the first spring and second fall semesters. Every student must register for the internship - 11-934 (MSAII Practicum Internship). No tuition is charged for the internship.

Electives (36 units)

You must take at least three 12-unit elective courses or equivalent. The approved electives are listed below:

• 11-641 Machine Learning for Text Mining
• 11-642 Search Engines
• 11-747 Neural Networks for NLP
• 11-755 Machine Learning for Signal Processing
• 11-777 Advanced Multimodal Machine Learning
• 10-605 Machine Learning with Large Datasets
• 10-608 Conversational Machine Learning
• 10-716 Advanced Machine Learning: Theory & Methods (was 10702)
• 15-624 Foundations of Cyber-Physical Systems
• 15-645 Database Systems
• 15-688 Practical Data Science
• 15-719 Advanced Cloud Computing
• 15-780 Graduate Artificial Intelligence
• 16-720 Computer Vision
• 16-725 Medical Image Analysis
• 16-722 Sensing and Sensors
• 16-824 Visual Learning and Recognition
• 17-637 Web Application Development
• 17-639 Management of Software Development
• 17-653 Managing Software Development
• 17-766 Software Engineering for Startups
• 02-604 Fundamentals of Bioinformatics
• 02-718 Computational Medicine

Admissions

The School of Computer Science requires the following for all applications:

• A GPA of 3.0 or higher.
• GRE scores: GRE is required.
• TOEFL/IELTS/Duolingo scores: If you are an international applicant and your native language (language spoken from birth) is not English, an official copy of English proficiency score report is required.
• Unofficial transcripts from each university you have attended, regardless of whether you received a degree.
• Current resume.
• Statement of Purpose.
• Three letters of recommendation.
• A short (2-3 minutes) video of yourself.

Application Deadlines

• Applications open on September 3, 2025
• Early Deadline: Nov. 19, 2025 (3 p.m. EST)
• Final Deadline: Dec. 10, 2025 (3 p.m. EST)