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.

Here's a detailed breakdown of the curriculum.

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. (This course is the distance education version of 15-213 Introduction to Computer Systems.) Failure to pass the course means that you have to take 15-213 during either the fall or spring semester, and the units will not count toward your 192 eligible units of study.

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.  In this course, open only to MSAII students, the class is divided into teams to survey 48 fields in which AI has been applied, make presentations to the faculty and fellow students on areas that are ripe for AI development.  Every four weeks the teams are completely permuted to give students the opportunity to work with as many other members of the class as possible.

  2. 17-762, Law of Computer Technology (12 units).  First fall semester.  A review of legal principles applicable to computer developments, including AI law and formation of startups.

  3. 11-695, AI Engineering (12 units).  First spring semester.  This course is devoted to integrating AI with legacy systems.  Topics include supervised learning, feed-forward neural networks, flow graphs, dynamic computational graphs, convolutional neural networks and recurrent neural networks.  Students will use high-level tools to engineer functioning machine learning models.

  4. 11-654, AI Innovation (12 units).  Second fall semester.  This is where the Capstone really begins.  Students are offered a number of projects (usually 6 or 7) sponsored by outside companies.  In the past, sponsors have been companies such as Bank of New York Mellon, John Deere, Flexport, the Pittsburgh International Aiport, and  The class, which is only open to MSAII students, is divided into teams, one for eah project.  The teams then work with their sponsor to understand the business environment of the project, identify dstakeholders, and design a Minimum Viable Product (MVP) that they will build out duirng the spring semester in the Capstone Project course 11-699.

  5. 11-699, Capstone Project (36 units).  Second spring semester. The objective of the Capstone is for your team to develop a working product suitable for intrapreneurial integration into a company or suitable for startup investment.

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.  (Normally 11-691, Math for Machine Learning, which is not being offered in Fall 2019.)

  3. 11-785, Deep Learning (12 units).  First spring semester.

  4. 10-623 Generative AI (12 units).  First spring semester.  OR 11-667, Large Language Models (12 units).  Second fall semester.

  5. 11-611, Natural Language Processing (12 units).  Second fall semester.

  6. A 12-unit course in AI, NLP, or ML.  Second fall semester.   This can be 10-605, Machine Learning with Large Datasets, 11-697 Introduction to Question Answering (with LLMs), 11-767 (On-Device Machine Learning), 11-777 (Multimodal Machine Learning), 11-851 Talking to Robots, or another AL, NLP or ML graduate course.

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, and you are granted 3 academic credits for it.

Electives (36 units)
You must take at least three 12-unit elective courses or equivalent. The approved electives are listed below.  If you want to take any other course for elective credit, you must have the permission of the MSAII Director.  It is recommended to take one elective in the first fall semester, one or two in the first spring semester, one or two in the second fall semester and zero or one in the second spring semester.

02-604 Fundamentals of Bioinformatics
02-718 Computational Medicine
10-605 Machine Learning with Large Datasets
10-608 Conversational Machine Learning 11-641 Machine Learning for Text Mining
10-716 Advanced Machine Learning: Theory & Methods (was 10702)11-642 Search Engines
11-747 Neural Networks for NLP
11-755 Machine Learning for Signal Processing
11-777 Advanced Multimodal Machine Learning
15-619 Cloud Computing
15-640 Distributed 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