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Curriculum

The Master of Science in Artificial Intelligence (MSAI) is a full-time STEM-designated 30-point program.  Students begin in the fall semester and may complete the program in May, August, or December of the following year.  The MSAI program features:

  1. AI core courses (12 points)
  2. A specialized concentration (12 points)
  3. An additional 6 points of electives or an Interdisciplinary Capstone project. Students may alternatively complete 6 credits of faculty-supervised AI research.

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. 

The following course schedule outlines a three-semester course sequence
  • First Semester
    1. Artificial Intelligence course; choose 1, 3 credits:
      1. COMS W4701 | Artificial Intelligence
      2. IEOR E4010 | Artificial Intelligence for Operations Research and Financial Engineering (option for Finance/Operations Concentration)

    2. Machine Learning course; 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

      For students with machine learning experience, the following courses may be used to satisfy this requirement:

      1. BMEN E4470 | Deep Learning in Biomedical Signal Processing
      2. COMS W4776 | Neural Networks and Deep Learning
      3. ECBM E4040 | Neural Networks and Deep Learning
      4. IEOR E4742 | Deep Learning for Operations Research and Financial Engineering
    3. NLP or Computer Vision: choose 1, 3 credits:*
      1. COMS W4705 | Natural Language Processing
      2. COMS W4731 | Computer Vision I
      3. COMS W4732 | Computer Vision II
      4. ELEN E4830 | Digital Image Processing and Computer Vision
    4. ENGI E4000 Professional Development and Leadership, 0 credits. This degree requirement must be completed by the end of the second semester.
    5. 3 credits of electives for the concentration or general elective requirement. 

    *This can also be taken during the second semester

  • Second Semester

    Ethical AI: choose 1, 3 credits:

    1. COMS W4710 | Ethical and Responsible Artificial Intelligence
    2. ORCS E4201 | Policy for Privacy Technologies

    9-12 credits of electives for the concentration, capstone project (or supervised AI research) or general elective requirement. 

  • Third Semester

    Students must complete the remaining degree requirements.  Core courses are expected to be completed by the first and second terms.  During the third term, students are taking electives for the concentration, continuation of capstone project (or supervised AI research) or general electives.

Concentrations

Electives

  • Biomedical Engineering
    • 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
  • Chemical Engineering
    • CHEN E4020 | Protection of Industrial Intellectual Property
  • Computer Science
    • 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 CS (Advanced Topics in Computer Security)
    • COMS W4995 | Topics in CS (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 CS (Machine Learning and Climate)
    • COMS W6998 | Topics in CS (Reinforcement Learning LLMs)
    • CSEE W4121 | Computer Systems for Data Science
  • Environmental Engineering
    • EAEE E4000 | Machine Learning for Environmental Engineering and Science
  • Electrical Engineering & Computer 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 (Reinforcement Learning in Information Systems)
    • EECS E6893 | Topics in Information Processing (Big Data Analytics)
    • EECS E6894 | Topics in Information Processing (Hardware/Software Co-Design for Data Center Processing)
    • EECS E6895 | Topics in Information Processing (Advanced Big Data and Artificial Intelligence)
    • EECS E6981 | Topics in Information Processing (Operating, Distributed, and Runtime System Optimization through AI/ML Techniques)
    • EECS E6991 | Advanced Deep Learning
    • EECS E6992 | Deep Learning on Edge
  • Electrical Engineering
    • 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 (Reinforcement Learning)
    • ELEN E6908 | Embedded AI
  • Industrial Engineering and Operations Research
    • 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 (Agentic AI and Data Economy)
    • IEOR E8100 | Advanced Topics in IEOR (Diffusion Models AI & RL)
    • IEOR E8100 | Advanced Topics in IEOR (GenAI: Model Alignment)
    • ORCS E4200 | Data-Driven Decision Modeling
    • ORCS E4529 | Reinforcement Learning
  • Mechanical Engineering
    • MECE E4611 | Robotics Studio
    • MECE E4602 | Intro to Robotics
    • MECE E6615 | Advanced Robotic Manipulation
    • MEEC E6600 | Mathematics of Machine Learning, Signals, and Control