Artificial Intelligence (MS)

With the prevalence of artificial intelligence (AI) across society and its rapid advancement, there are unprecedented demands for talented graduates educated with solid AI foundational skills and novel applications in specific domains. 

The Master of Science in Artificial Intelligence (MSAI) combines core AI courses with concentrations developed by Columbia Engineering departments and partner schools. This structure allows students to build a shared technical foundation together with training in how AI is applied within a specific field. Concentrations include engineering-focused areas such as AI and Advanced Computing; AI and Finance and Operations; Robotics and Perception; AI Infrastructure; AI and UI/UX; and AI and Biomedical, as well as concentrations developed with partner schools in public health, medicine, architecture, statistics, and the arts.

 

Students must take at least 30 points of courses at Columbia University at or above the 4000 level. At least 18 points of courses must be taken at Columbia Engineering at or above the 4000 level. The MSAI requires completion of 12 points of Core AI Foundation, 12 points in a specific concentration, and 6 points of a capstone project or additional elective courses. Graduates are awarded a Master of Science from Columbia Engineering with a transcript notation of their concentration. 

M.S. students must complete the professional development and leadership course, ENGI E4000 PROF DEVELOPMENT＆LEADERSHIP, as a graduation requirement. 

1. First semester: Artificial Intelligence course; choose 1, 3 credits:
COMS W4701
ARTIFICIAL INTELLIGENCE
IEOR E4010
Artificial Intelligence for OR and FE
2. First semester: Machine Learning course; choose 1, 3 credits:
COMS W4771
MACHINE LEARNING
IEOR E4525
MACHINE LEARNING FE ＆ OPR
ELEN E4720
Machine Learning for Signals, Information and Data
COMS W4721
MACHINE LEARNING FOR DATA SCI
For students with documented machine learning coursework and prior approval from the MSAI advisors, the following courses may be used to satisfy this requirement:
BMEN E4470
Deep Learning for Biomedical Signal Processing
COMS W4776
Neural Networks ＆ Deep Learning
ECBM E4040
NEURAL NETWRKS ＆ DEEP LEARNING
IEOR E4742
Deep Learning for OR and FE
3. First or Second semester: NLP or Computer Vision: choose 1, 3 credits:
COMS W4705
NATURAL LANGUAGE PROCESSING
COMS W4731
Computer Vision I: First Principles
COMS W4732
Computer Vision II: Learning
ELEN E4830
DIGITAL IMAGE PROCESSING
4. Second semester: Ethical AI: choose 1, 3 credits:
COMS W4710
Ethical and Responsible AI
ORCS E4201
Policy for Privacy Technologies
ENGI E4000PROF DEVELOPMENT＆LEADERSHIP
Complete 1 concentration from the list below (4 courses, 12 credits)
Capstone Project or Electives, 6 credits

MS in AI Concentrations 

AI and Advanced Computing

Choose 4 graduate-level AI-related courses from computer science and AI from the approved elective pool (current offerings can be found on the MSAI program website)

AI Infrastructure

Choose 4 courses:
COMS E6424HARDWARE SECURITY
CSEE W4868SYSTEM-ON-CHIP PLATFORMS
EECS E4750Heterogeneous Computing for Signal and Data Processing
EECS E4764Artificial Intelligence of Things (AIoT)
ELEN E6772TOPICS IN NETWORKING
ELEN E6908TOPICS IN ELECTRICAL AND COMPUTER ENGINE
EECS E6981
EECS E6692TOPICS DATA-DRIVEN ANAL ＆ COMP
EECS E6894TOPICS-INFORMATION PROCESSING

AI and Finance and Operations

IEOR E4742Deep Learning for OR and FE
IEOR E4418TRANSPORTATION ANALYTICS ＆ LOGISTICS
IEOR E4530TOPICS IN OPERATIONS RESEARCH
ORCS E4200Data-driven Decision Modeling
IEOR E4650BUSINESS ANALYTICS
IEOR E4737AI Applications in Finance
IEOR E4703MONTE CARLO SIMULATION METHODS
IEOR E4011Agentic AI for Operations Research and Financial Engineering
IEOR E4108SUPPLY CHAIN ANALYTICS
IEOR E4704Foundations of Financial Technology
ORCS E4529Reinforcement Learning

Robotics and Perception

COMS W4731Computer Vision I: First Principles
COMS W4732Computer Vision II: Learning
COMS W4733COMPUTATIONAL ASPECTS OF ROBOTICS
MECE E4602INTRODUCTION TO ROBOTICS
MECE E4611ROBOTICS STUDIO
MECE E6615ROBOTIC MANIPULATION
MECE E6616ROBOT LEARNING
ELEN E6908TOPICS IN ELECTRICAL AND COMPUTER ENGINE
EECS E4764Artificial Intelligence of Things (AIoT)
EEME E6911Topics in Control

AI and UI/IX

COMS W4170USER INTERFACE DESIGN
COMS W41723D UI AND AUGMENTED REALITY
COMS W4901Projects in Computer Science
COMS W4995TOPICS IN COMPUTER SCIENCE
COMS W4995TOPICS IN COMPUTER SCIENCE
COMS E6173Virtual Reality and Augmented Reality
COMS E6178Human-Computer Interaction
COMS E6998TOPICS IN COMPUTER SCIENCE
COMS E6998TOPICS IN COMPUTER SCIENCE
ENGI E4502Design of UI/UX for Connected Systems
IEME E4200HUMAN-CENTERED DESIGN AND INNOVATION

AI and Biomedical

BMEN E4420SIGNAL MODELING
BMEN E4460Deep Learning in Biomedical Imaging
BMEN E4470Deep Learning for Biomedical Signal Processing
BMCS E4480Statistical machine learning for genomics
BMCS E4575High-dimensional statistics for biomedical data
ECBM E4060INTRO-GENOMIC INFO SCI ＆ TECH

AI and Health and Medicine

Take the following three mandatory classes:
BINF G4001
BINF G4011ACCULTURATN TO MED ＆ CLIN INFO
BINF GU4008 (Section 003 Special Topics in Biomedical Informatics)
Choose 1 course:
BINF G4003SYMBOLIC AI IN HEALTH CARE
BINF G4008 (Section 001 Special Topics in Biomedical Informatics )
BINF G4008 (Section 002 Special Topics in Biomedical Informatics)
BINF GU4019Computational Epidemiology
BINF GR5001Data Science for Mobile Health

AI and Public Health

Choose 4 courses:
BIST P8105Data Science I (Data Science I )
BIST P8106Data Science II (Data Science II )
BIST P8124Graphical Models for Complex Health Data (Graphical Models for Complex Health Data)
BIST P8160Topics in Advanced Statistical Computing (Topics in Advanced Statistical Computin)
BIST P8122Statistical Methods for Causal Inference (Statistical Methods for Causal Inference)
BIST P8119Advanced Statistical and Computational Methods in Genetics and Genomics (Advanced Statistical and Computational Methods in Genetics and Genomics)
EHSC P6351Introduction to Network Science (Introduction to Network Science)
EHSC P8334Computational Toxicology (Computational Toxicology)
EPID P8451Introduction to Machine Learning for Epidemiology and Public Health (Intro to Machine Learning for Epidemiology and Public Health )
EPID P8477Epi Modeling for Infectious Diseases (Epidemiologic Modeling for Infectious Disease)

Statistical Foundation in AI

STAT GR5701PROBABILITY ＆ STAT FOR DATA SC
STAT GR5702EXPLORATORY DATA ANALYSIS/VISUAL
STAT GR5703STAT INFERENCE ＆ MODELING
STAT GR5241STATISTICAL MACHINE LEARNING 1
STAT GR5242ADVANCED MACHINE LEARNING
STAT GR5244Unsupervised Learning
STAT GR6701FOUNDATIONS OF GRAPHICL MODELS
STAT GR5294 (Topics in Machine Learning & Artificial )
1

Students enrolled in this concentration may take STAT GR5241 (Statistical Machine Learning) in lieu of the Machine Learning courses listed in the Core Foundation courses. Students doing so will take three required courses from this concentration (instead of four) and one more AI elective course from SEAS from the elective pool.  Additionally, students may take any MA in Statistics courses with prior approval from the Statistics Department (to ensure preparation and seat availability).

AI and Arts, Creativity, and Media

COMS W4901Projects in Computer Science
FILM AF8305DIGITAL STORY TELLING I: History and Theory of Interactivity
FILM AF8310DIGITAL STORY TELLING II
FILM AF8315Digital Storytelling III: Immersive Production
FILM AF8316World-Building and Unbuilding
THEA AT6190CREATIVE CODING
FILM AF6810Coding for Media Studies
FILM GU4951NEW MEDIA ART
VIAR AV5603AI ＆ PHOTOGRAPHY
FILM GU4045Augmented Creativity: practical uses of AI in storytelling, art and design
ARTS AR6040Transformative Storytelling: Crafting Stories of Understanding in Conversation with Emerging Technologies

AI and Architecture and Urbanism

ARCH A4894SPATIAL UX (Spatial UX )
ARCH A4988CODING FOR SPATIAL PRACTICES (Coding for Spatial Practices )
ARCH A6968SEEING WITH ALGORITHMS (Seeing with Algorithms )
ARCH A4845GENERATIVE DESIGN (Generative Design)
ARCH A6956SPATIAL AI (Spatial AI)
PLAN A6118 (Leveraging Data and AI for Real Estate Development )
PLAN A6113EXPLORING URBAN DATA WITH MACHINE LEARNING (Exploring Urban Data with Machine Learning)

AI and Journalism

Semester 1 (6 Credits)
JOUR S6013 (Reporting for MS in AI )
Choose 1 from:
JOUR S6010 (Written Word Class)
JOUR S6015 (Image and Sound for MS/AI: Audio)
JOUR S6015 (Image and Sound for MS/AI: Video)
Semester 2 (6 Credits)
Choose 1 from:
JOUR6002 (S&P: Moderating the Internet)
JOUR6002 (S&P: News Products)
JOUR6002 (S&P: Telling Stories in Sound)
JOUR6002 (S&P: Multimedia Storytelling)
JOUR6002 (S&P: Data Visualization)
JOUR6002 (S&P: Information Warfare)