A curriculum built across the AI stack.

At Brown, AI is not taught in isolation.

The track brings together core areas of computer science to give students a comprehensive understanding of how modern AI systems are built and deployed. Students study computer systems and infrastructure alongside machine learning and deep learning, while also examining how AI interacts with people, organizations and society.

This full stack perspective prepares students to understand AI at every layer of the technology stack — and how those layers work together.

AI and Society Examine the legal, social and ethical dimensions of AI and the ways intelligent systems affect people and institutions.

Algorithms & Data Develop a deep foundation in machine learning, deep learning and the computational methods behind intelligent systems.

Systems Understand the hardware, infrastructure, and software systems that make modern AI possible.

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Learning Format

Whether you study on campus in Providence, RI or online from wherever you are, you’ll experience the same rigorous AI/ML curriculum, learn from the same distinguished Brown faculty and earn the same master of science in computer science degree.

Residential

• Study AI/ML on Brown’s campus in Providence and become part of a collaborative community of computer scientists, faculty and peers. The residential experience offers opportunities for in-person discussion, collaboration and direct engagement within Brown’s academic and research environment.

Online

• Continue your career while earning your degree. Online students in the AI/ML track take the same courses as residential students and learn from the same faculty through live, synchronous lectures and asynchronous learning activities, with regular opportunities to engage with faculty and classmates. Online students also have access to the same resources and events outside of the department through the Center for Master’s Student Excellence.

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Curriculum

Students take four core courses and four electives.

Core Curriculum

• Machine Learning
• Deep Learning
• Systems for AI and AI for Systems
• AI and Society

Electives

• Foundation courses to prepare for the core curriculum
  • Foundations of AI and Machine Learning
  • Fundamentals of Computer Systems
• Advanced electives subject to availability, for example:
  • Computer Vision
  • Computational Linguistics
  • Learning and Sequential Decision Making
  • Generative AI Law

See the full degree requirements for additional details.