CurriculumMSAI+X
MSAI+X is a 12-month, full-time program designed for students with an advanced degree—MD, PhD, or JD—who plan on continuing in their original field of study in addition to bringing the tools and techniques of 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.
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.
SUMMER QUARTER
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.
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 (MSAI 339)
Data models and database design.
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. Students take MSAI 431 in Fall and Winter quarters; each quarter is a half course unit.
Topics Course (MSAI 495)
A half-unit course on an AI-related topic, ranging from leadership to AI platforms.
Winter Quarter
Required core courses this quarter include classes in knowledge representation and commonsense reasoning, and collaborative system design.
Knowledge Representation and Reasoning (MSAI 371)
Problem solving, ontologies, reasoning.
Deep Learning (MSAI 437)
A hands-on introduction to deep networks, their varieties, applications, and algorithms used to train them.
Human Computer Interaction (MSAI 330)
Human-Computer Interaction.
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. Students take MSAI 431 in Fall and Winter quarters; each quarter is a half course unit.
Topics Course (MSAI 495)
A half-unit course on an AI-related topic, ranging from leadership to AI platforms.
Spring Quarter
This quarter students work in teams to complete a practicum project. Students also take a core course in semantic information processing and two electives.
Natural Language Processing (MSAI 337)
Depth of both understanding and generation systems. Focus on representation and inference.
Practicum in Intelligent Systems (MSAI 490)
A design and development experience in which students work to solve an open-ended Artificial Intelligence problem. It provides students with practical experience and helps to emphasize the importance of learning by doing. The project is subject to real-world constraints.
Two Elective Courses
Students will have an opportunity to choose two 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.
