MSAI+X: Alternate AI Curriculum for Holders of Advanced Degrees

Students with a prior advanced degree—MD, PhD, or JD—are able to complete a Master of Science in Artificial Intelligence (MSAI) degree with a shortened 12-month (4 quarter) curriculum with the approval of the MSAI program director.

This alternative track is designed for students who are planning on continuing in their original field of study and want to bring the tools and techniques to AI to help transform it. The envisioned role for these students once they exit is closer to product manager than developer. Our goal is to provide students with the skills they need to help in the responsible development of AI systems in their primary fields of work and to provide leadership as those technologies are both envisioned and deployed.

Students begin the program in the summer with a programming and mathematics bootcamp, then join students in the traditional curriculum in the fall quarter and take most of their class work together as part of the larger cohort. MSAI+X students complete their degree at the end of spring quarter.

Artificial Intelligence (AI) Fellowship in Cardiovascular Disease

A partnership between the Northwestern Medicine Bluhm Cardiovascular Institute and the McCormick School of Engineering is training the next generation of clinicians in the emerging area of AI. Each year, the MSAI+X program enrolls up to three fellows in cardiology, cardiac surgery, or internal medicine.


Prior to joining the cohort in the Fall, MSAI+X students acquire programming and mathematical knowledge and practice that will help them be successful in the program.

Intensive Program Design (MSAI 305)

A Python programming bootcamp designed for the inexperienced programmer to acquire a basic understanding of some core computer science concepts.

Linear Algebra (MATH 240)*

An introduction to the field of linear algebra: Gaussian elimination; vectors and matrices; vector spaces, subspaces, linear independence, bases, and linear transformations; and geometric interpretations of linear transformations in Euclidean n-spaces.

Introduction to Statistics (STAT 202)*

Basic concepts of using statistical models to draw conclusions from experimental and survey data. Topics include simple linear regression, multiple regression, analysis of variance, and analysis of covariance.

* With approval of the MSAI program director, students may be able to waive or replace Linear Algebra and Introduction to Statistics.

Fall Quarter

Students take a set of required core courses to establish a baseline body of knowledge for all in the cohort. This quarter focuses on a deep introduction to AI, machine learning and interactive AI systems, and on human cognition.

Frameworks for Artificial Intelligence (MSAI 431)

Framing artificial intelligence to explore the latest challenges in the theory, practice and implications of AI in the modern world.

Introduction to AI (MSAI 348)

Core techniques and applications of artificial intelligence.

Machine Learning (MSAI 349)

The study of algorithms that improve automatically through experience.

Data Science Seminar (MSAI 339)

Data models and database design.

Winter Quarter

Required core courses this quarter include classes in semantic information processing, knowledge representation and commonsense reasoning, and collaborative system design.

Natural Language Processing (MSAI 337)

Depth of both understanding and generation systems. Focus on representation and inference.

Knowledge Representation and Reasoning (MSAI 371)

Problem solving, ontologies, reasoning.

Deep Learning Foundations from Scratch (MSAI 495)

A hands-on introduction to deep networks, their varieties, applications, and algorithms used to train them.

Human Computer Interaction (MSAI 330)

Human-Computer Interaction.

Spring Quarter

This quarter students enroll in three elective courses in relevant or related areas to Artificial Intelligence while teaming to complete an industry-led practicum project guided by a corporate partner and overseen by an academic and technical adviser. Students will choose from elective courses in Computer Science or with MSAI director approval, from courses offered through other academic departments across the university, including the Kellogg School of Management.

Practicum in Intelligent Systems (MSAI 490)

A design and development experience in which students work to solve an open-ended Artificial Intelligence problem in an area they care about. It provides students with practical experience and helps to emphasize the importance of learning by doing. The project is subject to real-world constraints.

Three Elective Courses

Students will have an opportunity to choose three elective courses from a variety of options, including Introduction to Robotics, Data Management and Information Processing, Designing and Constructing Models with Multi-Agent Languages, Active Learning in Robotics and Seminar in Statistical Language Modeling.


Speaker Series

Along with classes, the program hosts an ongoing series of speakers from industry to provide students with a view of the business problems of today and how the technologies and techniques they are learning about can be applied.