Columbia University MS in Artificial Intelligence

Columbia University offers a Master of Science in Artificial Intelligence (MSAI) through the Fu Foundation School of Engineering and Applied Science.

The 30-point program is offered full time on the New York City campus and part time online. Every student completes a 12-point core covering artificial intelligence, machine learning, natural language processing or computer vision, and ethical AI, then adds a 12-point concentration that applies AI to a specific field.

Last reviewed: October 2026

Quick Facts

DegreeMS in Artificial Intelligence (MSAI)
SchoolFu Foundation School of Engineering and Applied Science
LocationNew York, New York or Online
FormatFull time on campus or part time online
Credits30 points
Concentrations11 on campus, 3 online
Culminating ExperienceOptional two-semester capstone or faculty-supervised research
Minimum GPANo minimum published
GREOptional
Tuition$2,822 per point on campus (2026–27); $2,700 per credit online
Campus DeadlinesJanuary 15 (priority), February 15 (regular) for fall
Typical LengthTwo to three semesters on campus; five semesters online
STEM DesignatedYes

Official MS in Artificial Intelligence page

Curriculum

The MSAI is a new degree, with its first on-campus students starting in fall 2026. It is separate from the Machine Learning pathway in Columbia’s MS in Computer Science and from the MS in Data Science offered through the Data Science Institute.

Students complete 30 points consisting of:

  • 12 points of AI core courses
  • 12 points in a concentration
  • 6 points of general electives, a two-semester interdisciplinary capstone project, or engineering faculty-supervised AI research

Students also complete ENGI E4000 – Professional Development and Leadership, a zero-credit graduation requirement. The concentration is noted on the transcript.

Core Courses

On-campus students take one 3-point course from each of four groups:

  • Artificial intelligence: COMS W4701 – Artificial Intelligence or IEOR E4010 – Artificial Intelligence for Operations Research and Financial Engineering
  • Machine learning: COMS W4771 – Machine Learning, IEOR E4525 – Machine Learning for OR and FE, or ELEN E4720 – Machine Learning for Signals, Information, and Data
  • Natural language processing or computer vision: COMS W4705 – Natural Language Processing, COMS W4731 – Computer Vision I, COMS W4732 – Computer Vision II, or ELEN E4830 – Image Processing and Computer Vision
  • Ethical AI: COMS W4710 – Ethical and Responsible Artificial Intelligence or ORCS E4201 – Policy for Privacy Technologies

Students who already have machine learning experience may meet the machine learning requirement with a deep learning course instead, such as COMS W4776 – Neural Networks and Deep Learning or ECBM E4040 – Neural Networks and Deep Learning.

Online students take a fixed core with no alternates:

  • COMS W4701 – Artificial Intelligence
  • COMS W4771 – Machine Learning
  • COMS W4705 – Natural Language Processing
  • COMS W4710 – Ethical and Responsible Artificial Intelligence

Concentrations

The on-campus program lists 11 concentrations:

  • AI and Advanced Computing
  • Robotics and Perception
  • AI and Finance and Operations
  • AI and Biomedical
  • AI Infrastructure
  • AI and UI/UX
  • AI and Health and Medicine
  • AI and Public Health
  • AI and Arts, Creativity, and Media
  • Statistical Foundation in AI
  • AI and Architecture and Urbanism

The online program offers three of these: AI in Finance and Operations, AI Infrastructure, and AI and Advanced Computing.

AI and Advanced Computing Courses

In the AI and Advanced Computing concentration, students choose two courses from a computer science list and two from an AI-related engineering list. Options include:

  • COMS W4773 – Machine Learning Theory
  • COMS W4774 – Unsupervised Learning
  • COMS W4775 – Causal Inference
  • COMS W4776 – Neural Networks and Deep Learning
  • COMS W4733 – Computational Aspects of Robotics
  • EECS E6694 – GenAI and Modern Deep Learning
  • EECS E6699 – Mathematics of Deep Learning
  • EECS E6720 – Bayesian Models in ML
  • ORCS E4529 – Reinforcement Learning
  • IEOR E4540 – Data Mining
  • IEOR E6617 – Machine Learning and High-Dimensional Data
  • BMEN E4460 – Deep Learning in Biomedical Imaging
  • ELEN E6772 – Machine Learning for Computer and Communications Networks
  • MECE E4602 – Intro to Robotics

Deep learning, data mining, and reinforcement learning are electives rather than core courses. Each concentration has its own course list, so the mix differs for students in finance, health, or media concentrations.

Capstone and Research

The capstone is optional and runs for two semesters. Students work with a faculty mentor and industry partners. The first semester covers project management, team formation, and problem identification, and the second covers development, testing, and final evaluation. Students can instead use the 6 points for supervised AI research or general electives.

Cost and Tuition

Columbia is a private university and charges the same tuition regardless of state residency. On-campus and online students are billed at different per-credit rates.

Columbia lists these rates:

CategoryRatePublished Total
On campus, per point (2026–27)$2,822$67,728 for 24 points over two semesters
Online, per credit (2026)$2,700$81,000 for 30 credits

At 30 points, on-campus tuition comes to about $84,660 before fees. For 2026–27, the engineering school’s budget for full-time master’s students also lists a $1,518 health service fee, a $1,290 university services and support fee, a $960 graduate student activities fee, and a one-time $105 document fee. Health insurance is $5,903 and can be waived with other coverage. International students pay a $350 international student fee.

Students in the MS in Artificial Intelligence program also pay an additional $1,144 program fee.

The online program has no application fee and asks admitted students for a $2,000 deposit. Rates can change each year, so students should confirm current costs with Columbia.

View Columbia graduate engineering cost of attendance

Admission Requirements

Applicants need a bachelor’s degree from an institution of acceptable standing. An undergraduate STEM degree is recommended but not required.

Columbia Engineering states that there is no minimum GPA or minimum test score required to apply.

Applicants are expected to have a background in:

  • programming, especially Python
  • data structures and algorithms
  • linear algebra
  • calculus and probability

The on-campus program says it will provide a pathway for admitted students who need preparatory or bridge courses.

Application Materials

On-campus applicants submit:

  • online application and $85 application fee
  • transcripts from every post-secondary institution attended
  • three letters of recommendation
  • personal statement
  • resume or CV
  • proof of English proficiency when required

The GRE is optional. Columbia Engineering states that applicants who do not submit scores are not penalized. An interview may be requested.

Online applicants submit the same core materials plus a video interview, and pay no application fee.

Application Deadlines

ProgramEntry TermDeadline
On campusFallJanuary 15 (priority), February 15 (regular)
OnlineSpring 2027December 1 (early), January 8 (regular)
OnlineFall 2027June 15 (early), August 17 (regular)

On-campus students begin in the fall semester. Columbia Engineering does not offer deferrals, so applicants should apply for the term they plan to enroll.

Program Format

The on-campus program is full time in New York City. Students start in the fall and may finish in May, August, or December of the following year. The published plan covers three semesters.

The online program is part time and fully online. Students move through five consecutive semesters as a cohort, including summer terms, and finish in under two years. Lectures are recorded, and Columbia estimates about 24 hours of work per week.

The on-campus program is STEM designated. On-campus students may take internships part time during the fall and spring and full time in the summer.

Who Is This Program Best For?

Columbia’s MS in Artificial Intelligence is a strong option for STEM graduates who want a required AI and machine learning core followed by a concentration in a field where they plan to apply it.

It may be especially appealing to students interested in:

  • machine learning and artificial intelligence as required coursework
  • natural language processing or computer vision
  • applying AI in finance, health, biomedical engineering, robotics, or media
  • AI infrastructure and systems
  • an optional industry capstone or faculty-supervised research
  • a part-time online route with the same degree

The degree is new, with its first class starting in fall 2026, and tuition is above $80,000 on either route. The online version offers three concentrations instead of eleven, and there is no thesis track. Students who want a lower-cost degree, a thesis, or a broader computer science curriculum may want to compare other New York schools or Columbia’s MS in Computer Science with its Machine Learning pathway.

Students can also compare Columbia University with other master’s programs in machine learning, artificial intelligence, and related fields offered in New York.

View All Machine Learning Programs in New York