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.
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.
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:
- 11-651, Artificial Intelligence and Future Markets (12 units). First fall semester. In this course, students are divided into teams to survey the fieild of AI applications, make presentations to the faculty and fellow students on areas that are ripe for AI development, and must develop a product proposal, which will be carried through for the next three semesters, leading to 11-699, the Capstone Project.
- 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.
- 11-695, AI Engineering (12 units). First spring semester. This course is devoted to building deep learning applications using TensorFlow and Python. 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.
- 11-654, AI Innovation (12 units). Second fall semester. Students learn how to build an enterprise, either intrapreneurial or entrepreneurial, by developing a business model and strategy for their team's product.
- 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.
- 11-601, Coding Bootcamp (12 units). First fall semester.
- 10-601, Machine Learning (12 units), First fall semester. (Normally 11-691, Math for Machine Learning, which is not being offered in Fall 2019.)
- 10-605, Machine Learning with Large Datasets (12 units). First spring semester.
- 11-611, Natural Language Processing (12 units). Second fall semester.
- A 12-unit course in AI, NLP, or ML. Second fall semester.
- 11-785, Deep Learning (12 units). Second spring semester.
Every student is required to complete an industry internship during the summer between the first spring and second fall semesters.
Every student is required to register for 11-935, the LTI Practicum during the summer between the first spring and second fall semesters. This is required to maintain your student status and no tuition is charged.
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.
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
11-791 Design of Intelligent Information Systems
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
15-681 AI: Representation and Problem Solving
15-688 Practical Data Science
15-719 Advanced Cloud Computing
16-720 Computer Vision
16-725 Medical Image Analysis
16-772 Sensing and Sensors
16-824 Visual Learning and Recognition
17-637 Web Application Development
17-639 Management of Software Development
17-652 Methods: Deciding What to Design
17-653 Managing Software Development
17-766 Software Engineering for Startups
02-604 Fundamentals of Bioinformatics
02-718 Computational Medicine