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Master of Science in Artificial Intelligence Curriculum

The Master of Science in Artificial Intelligence is a full-time STEM-designated 30-point program, which includes core courses (12 points), four courses in a specialized concentration (12 points), and either an Interdisciplinary Capstone project or additional electives (6 points). With approval from the faculty advisor, students may alternatively complete 6 credits of faculty-supervised AI research aligned with their concentration, which may be applied toward the elective requirement.

Students must also complete the professional development and leadership course, ENGI E4000, as a graduation requirement. 

Students completing the program will be awarded a Master of Science in Artificial Intelligence degree and receive an annotation of the specific concentration on their transcript/diploma. 

Students begin in the fall semester and may complete the program in May, August, or December of the following year.

Overview

Foundation

Students take a series of four core AI courses followed by four courses in a concentration, and a capstone project or 2 additional elective courses.

The core courses cover the fundamentals as well as advanced, cutting-edge developments in artificial intelligence, including machine learning, deep learning, natural language processing, and computer vision. 

Sample Topics

Students choose a concentration from among graduate-level courses offered in the School of Engineering and Applied Science and across Columbia University. 

Eleven concentrations have been prepared, and more are being added.

Electives and Capstone

In addition to a large list of approved electives, the Master of Science in Artificial Intelligence allows for a culminating, two-semester, capstone project integrating the skills developed in the core curriculum with the knowledge from the elective concentration. 

The independent research component of the Capstone is conducted in close collaboration with one or more faculty advisors and builds directly on the skills developed in project management, team formation, and conflict resolution.

Capstone projects are expected to have strong engagement from industry partners to ensure that projects address real-world challenges. 

Students in cross-school concentrations will work with both an Engineering faculty mentor and a faculty member from the partner school.  

Required Courses and Electives

  • Program Requirements

    The Master's of Science in Artificial Intelligence requires 30 credits of coursework, with at least 18 credits being taken at Columbia Engineering.

    1. COMS W4701 Artificial Intelligence, 3 credits
    2. Choose 1, 3 credits:
      1. COMS W4771 Machine Learning
      2. IEOR E4525 Machine Learning for OR & FE
      3. ELEN E4720 Machine Learning for Signals, Information and Data
      4. COMS W4721 Machine Learning for Data Science
    3. Choose 1, 3 credits:
      1. COMS W4705 Natural Language Processing
      2. COMS W4731 Computer Vision I
      3. COMS W4732 Computer Vision II
    4. COMS W4710 Ethical and Responsible Artificial Intelligence, 3 credits
    5. ENGI E4000 Professional Development and Leadership, 0 credits
    6. Complete 1 concentration from the list below (4 courses, 12 credits)
    7. Capstone Project or Electives, 6 credits
  • Electives

    Students who choose not to participate in the Capstone project would choose two courses (6 credits) from the list of approved electives below:

    • BMCS E4480 | Statistical Machine Learning for Genomics
    • BMEN E4460 | Deep Learning in Biomedical Imaging
    • BMEN E4470 | Deep Learning for Biomedical Signal Processing
    • CBMF W4761 | Computational Genomics
    • CHEN E4020 | Protection of Industrial Intellectual Property
    • COMS W4460 | Principle Innovation Entrepreneurship
    • COMS W4706 | Spoken Language Processing
    • COMS W4731 | Computer Vision I
    • COMS W4732 | Computer Vision II
    • COMS W4775 | Casual Inference
    • COMS W4776 | Neural Networks and Deep Learning
    • COMS W4901 | AI and Storytelling
    • COMS W4995 | Topics In Computer Science (Topic: Adv Tpcs Comp Security)
    • COMS W4995 | Topics In Computer Science (Topic: Data-Driven Design for Social Innovation)
    • COMS W6113 | Agentic Systems Made Real
    • COMS W6706 | Advanced Spoken Language Processing
    • COMS W6975 | Advanced Natural Language Processing
    • COMS W6998 | Topics In Computer Science (Topic: Machine Learning & Climate)
    • COMS W6998 | Topics In Computer Science (Topic: Reinforcement Learning LLMs)
    • CSEE W4121 | Computer Systems for Data Science
    • EAEE E4000 | Machine Learning for Environmental Engineering and Science
    • ECBM E4040 | Neural Networks & Deep Learning
    • EECS E4750 | Heterogeneous Computing for Signal and Data Processing
    • EECS E4764 | Artificial Intelligence of Things
    • EECS E6694 | GenAI and Modern Deep Learning
    • EECS E6699 | Mathematics of Deep Learning
    • EECS E6720 | Bayesian Models in ML
    • EECS E6870 | Speech Recognition
    • EECS E6892 | Topics in Information Processing (Topic: Reinforcement Learning in Information Systems)
    • EECS E6893 | Topics in Information Processing (Topic: Big Data Analytics)
    • EECS E6894 | Topics in Information Processing (Topic: Hardware/Software Co-Design for Data Center Processing)
    • EECS E6895 | Topics in Information Processing (Topic: Advanced Big Data and Artificial Intelligence)
    • EECS E6981 | Topics in Information Processing (Topic: Operating, Distributed, and Runtime System Optimization through AI/ML Techniques)
    • EECS E6991 | Advanced Deep Learning
    • EECS E6992 | Deep Learning on Edge
    • ELEN E4620 | Numerical Methods for Data Analysis
    • ELEN E4730 | Quantum Optimization and Machine Learning
    • ELEN E4830 | Digital Image Processing
    • ELEN E6772 | Machine Learning for Computer and Communications Networks
    • ELEN E6820 | Speech & Audio Processing & Recognition
    • ELEN E6876 | Sparse and Low-Dimensional Models for High-Dimensional Data
    • ELEN E6885 | Topics in Signal Processing (Topic: Reinforcement Learning)
    • ELEN E6908 | Embedded AI
    • IEOR E4530 | AI & Games & Markets
    • IEOR E4540 | Data Mining
    • IEOR E4550 | Entrepreneurial Business Creation
    • IEOR E4650 | Business Analytics
    • IEOR E4703 | Monte Carlo Simulation Methods
    • IEOR E4737 | AI Applications in Finance
    • IEOR E4742 | Deep Learning for OR & FE
    • IEOR E4998 | Managing Technological Innovation and Entrepreneurship
    • IEOR E6529 | Advanced Reinforcement Learning
    • IEOR E6617 | Machine Learning and High-Dimensional Data
    • IEOR E8100 | Advanced Topics In IEOR (Topic: Agentic AI and Data Economy)
    • IEOR E8100 | Advanced Topics In IEOR (Topic: Diffusion Models AI & RL)
    • IEOR E8100 | Advanced Topics In IEOR (Topic: GenAI: Model Alignment)
    • MECE 4611 | Robotics Studio
    • MECE E4602 | Intro to Robotics
    • MECE E6615 | Advanced Robotic Manipulation
    • MEEC E6600 | Mathematics of Machine Learning, Signals, and Control
    • ORCS E4200 | Data-Driven Decision Modeling
    • ORCS E4529 | Reinforcement Learning

Concentrations

  • AI and Advanced Computing

    Choose 4 courses: minimum 2 graduate-level AI-related courses from Computer Science and 2 AI-related non-CS courses in Engineering from the approved elective pool below.

    Computer Science courses

    Choose 2 courses:

    • BMCS E4480 | Statistical Machine Learning for Genomics
    • COMS W4731 | Computer Vision I
    • COMS W4732 | Computer Vision II
    • COMS W4733 | Computational Aspects of Robotics
    • COMS W4773 | Machine Learning Theory
    • COMS W4774 | Unsupervised Learning
    • COMS W4775 | Causal Inference
    • COMS W4776 | Neural Networks and Deep Learning
    • COMS W4995 | Topics In Computer Science (Topic: Deep Learning for Computer Vision)
    • COMS W6706 | Advanced Spoken Language Processing
    • COMS W6975 | Topics in Natural Language Processing
    • COMS E6998 | Topics in Computer Science (Topic: LLM-Based Generative AI Systems)
    • COMS E6998 | Topics in Computer Science (Topic: High-Performance Machine Learning)
    • COMS E6998 | Topics in Computer Science (Topic: Scaling LLMs)
    • COMS E6998 | Topics in Computer Science (Topic: Designing for Generative AI)
    • EECS E4764 | Artificial Intelligence of Things
    • EECS E6991 | Advanced Deep Learning
    • EECS E6992 | Deep Learning on Edge
    • EECS E6694 | GenAI and Modern Deep Learning
    • EECS E6699 | Mathematics of Deep Learning
    • EECS E6720 | Bayesian Models in ML
    • ORCS E4200 | Data-Driven Decision Modeling
    • ORCS E4529 | Reinforcement Learning
    AI-related in Engineering

    Choose 2 courses:

    • BMEN E4460 | Deep Learning in Biomedical Imaging
    • BMEN E4470 | Deep Learning for Biomedical Signal Processing
    • EAEE E4000 | Machine Learning for Environmental Engineering and Science
    • ECBM E4040 | Neural Networks & Deep Learning
    • ELEN E4730 | Quantum Optimization and Machine Learning
    • ELEN E6772 | Machine Learning for Computer and Communications Networks
    • IEOR E4540 | Data Mining
    • IEOR E4530 | AI & Games & Markets
    • IEOR E4742 | Deep Learning for OR & FE
    • IEOR E6529 | Advanced Reinforcement Learning
    • IEOR E6617 | Machine Learning and High-Dimensional Data
    • IEOR E8100 | Advanced Topics In IEOR (Topic: Agentic AI and Data Economy)
    • IEOR E8100 | Advanced Topics In IEOR (Topic: Diffusion Models AI & RL)
    • IEOR E8100 | Advanced Topics In IEOR (Topic: GenAI: Model Alignment)
    • MECE E4602 | Intro to Robotics
    • MECE E6615 | Advanced Robotic Manipulation
    • MEEC E6600 | Mathematics of Machine Learning, Signals, and Control
  • AI Infrastructure

    Choose 4 courses:

    • COMS E6424 | Hardware Security
    • CSEE W4121 | Computer Systems for Data Science
    • CSEE W4868 | System-On-Chip Platforms
    • EECS E4750 | Heterogeneous Computing for Signal and Data Processing
    • EECS E4764 | Artificial Intelligence of Things
    • EECS E6692 | Topics in Data-Driven Anal & Comp
    • EECS E6894 | Topics in Information Processing
    • EECS E6981 | Operating, Distributed, and Runtime System Optimization through AI/ML Techniques
    • ELEN E6772 | Machine Learning for Computer and Communication Networks
    • ELEN E6908 | Embedded AI
  • AI and Finance and Operations

    Choose 4 courses:

    • IEOR E4418 | Transportation Analytics & Logistics
    • IEOR E4530 | AI & Games & Markets
    • IEOR E4650 | Business Analytics
    • IEOR E4703 | Monte Carlo Simulation Methods
    • IEOR E4737 | AI Applications in Finance
    • IEOR E4742 | Deep Learning for Operations Research & Financial Engineering
    • ORCS E4200 | Data-Driven Decision Modeling
  • Robotics and Perception

    Choose 4 courses:

    • COMS W4731 | Computer Vision I
    • COMS W4732 | Computer Vision II
    • COMS W4733 | Computational Aspect of Robotics
    • EECS E4764 | Artificial Intelligence of Things
    • EEME E6911 | Probabilistic Robotics
    • ELEN E6908 | Embedded AI
    • MECE E4602 | Intro to Robotics
    • MECE 4611 | Robotics Studio
    • MECE E6615 | Advanced Robotic Manipulation
    • MECE E6616 | Robot Learning
  • AI and UI/UX

    Choose 4 courses:

    • COMS E6173 | Virtual Reality and Augmented Reality
    • COMS E6178 | Human–Computer Interaction
    • COMS E6998 | Topics In Computer Science: Design for Generative AI
    • COMS E6998 | Topics In Computer Science: Systems for Human–Data Interaction
    • COMS W4170 | User Interface Design
    • COMS W4172 | 3D User Interfaces & Augmented Reality
    • COMS W4901 | AI and Storytelling
    • COMS W4995 | Topics In Computer Science: Data-Driven Design for Social Innovation
    • COMS W4995 | Topics In Computer Science: Introduction to Data Visualization
  • AI and Biomedical

    Choose 4 courses:

    • BMCS E4480 | Statistical Machine Learning for Genomics
    • BMCS E4575 | High-Dimensional Statistics for Biomedical Data
    • BMEN E4420 | Biomedical Signal Modeling
    • BMEN E4460 | Deep Learning in Biomedical Imaging
    • BMEN E4470 | Deep Learning for Biomedical Signal and Sequence Analysis
    • ECBM E4060 | Intro-Genomic Info Sci & Tech
  • AI and Health and Medicine

    Take the following three mandatory classes:

    • BINF G4001 | Intro Comp Biomed & Health
    • BINF G4011 | Acculturation to Med & Clin Info
    • BINF GU4008 003 |  Special Topics in Biomedical Informatics

    Choose 1 course:

    • BINF G4003 | Methods III: Symbolic AI for Health Care
    • BINF G4008 001 |  Special Topics in Biomedical Informatics
    • BINF G4008 002 | Special Topics in Biomedical Informatics
    • BINF G4019 | Computational Epidemiology
    • BINF G5001 | Data Science for Mobile Health

     

  • AI and Public Health

    Choose 4 courses:

    • BIST P8105 | Data Science I
    • BIST P8106 | Data Science II
    • BIST P8119 | Advanced Statistical and Computational Methods in Genetics and Genomics
    • BIST P8122 | Statistical Methods for Causal Inference
    • BIST P8124 | Graphical Models for Complex Health Data
    • BIST P8160 | Topics in Advanced Statistical Computing
    • EHSC P6351 | Introduction to Network Science
    • EHSC P8334 | Computational Toxicology
    • EPID P8451 | Intro to Machine Learning for Epidemiology and Public Health
    • EPID P8477 | Epidemiologic Modeling for Infectious Disease 
  • Statistical Foundation in AI

    Choose 4 courses:

    • STAT GR52411 | Statistical Machine Learning
    • STAT GR5242 | Advanced Machine Learning
    • STAT GR5244 | Unsupervised Learning
    • STAT GR5294 | Topics in Machine Learning & Artificial
    • STAT GR5701 | Probability and Statistics for Data Science
    • STAT GR5702 | Exploratory Data Analysis and Visualization
    • STAT GR5703 | Statistical Inference and Modeling
    • STAT GR6701 | Probabilistic Models and Machine Learning

    1Students 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

    Choose 4 courses:

    • ARTS AR6040 | Transformative Storytelling
    • COMS W4901 | AI and Storytelling
    • FILM AF6810 | Coding for Media Studies
    • FILM AF8305 | Digital Storytelling I: History and Theory of Interactivity
    • FILM AF8310 | Digital Storytelling II: Art, Craft, and Business of Storytelling
    • FILM AF8315 | Digital Storytelling III: Immersive Production
    • FILM AF8316 | World-building and Unbuilding
    • FILM GU4045 | Augmenting Creativity with AI
    • FILM GU4951 | New Media Art
    • THEA AT6190 | Creative Coding
    • VIAR AV5603 | AI and Photography
  • AI and Architecture and Urbanism

    Choose 4 courses:

    • ARCH A4845 |  Generative Design
    • ARCH A4894 | Spatial UX
    • ARCH A4988 | Coding for Spatial Practices
    • ARCH A6956 | Spatial AI
    • ARCH A6968 | Seeing with Algorithms
    • PLAN A6113 | Exploring Urban Data with Machine Learning
    • PLAN A6118 | Leveraging Data and AI for Real Estate Development