Graduate Programs and Courses in AI

Academic departments at Stony Brook offer a rich variety of graduate courses and programs in Artificial Intelligence and related fields. 

Program offerings

Graduate Program in Data Science

The SBU Graduate Program in Data Science (DAS) features both MS and PhD degree programs in Data Science. It is jointly offered by the Department of Applied Mathematics and Statistics (AMS), and the Department of Computer Science (CS), both part of the College of Engineering and Applied Sciences (CEAS). Students will receive vigorous training in Data Science encompassing topics such as statistical analysis, big data analysis/management and fundamentals of computing.

Specialization: MS with Concentration in Data Science and Engineering

Large-scale data generated by humans and machines is available everywhere. Acquiring the fundamental skills on how to 1) analyze and understand as well as 2) manage and process these large datasets is crucial in today's data- driven world for producing data products that solve real-world problems.

Through this concentration students learn the fundamental concepts in data science and develop a skill-set needed to become data scientists. Major areas covered through thought-provoking classes include distributed data management, basics of probability, visualization, statistical learning, scalability, and optimization.

Engineering Artificial Intelligence MS Program

The Master of Science in Engineering of Artificial Intelligence (EAI) prepares specialists with comprehensive knowledge in all areas of this new disruptive and revolutionary technology. The program provides interdisciplinary foundations and practical experience in algorithms, sensors, hardware, control, and applications. The program consists of a three-semester course sequence which covers the fundamentals of Artificial Intelligence, probabilistic reasoning, machine learning, deep learning algorithms, sensor electronics, digital systems design and acceleration hardware, control theory and practice, convex optimization, natural language processing, and computer vision and applications in mobile, health, and other domains. The holistic nature of the program allows students to specialize in any sub-field of Artificial Intelligence (AI) and solve real world problems, many of which go beyond just algorithms and software.

Course offerings

Applied Mathematics and Statistics

Courses

  • AMS 515: Case Studies in Machine Learning and Finance
  • AMS 520: Machine Learning in Quantitative Finance
  • AMS 524: Modern Computational Data Analytics
  • AMS 572: Data Analysis
  • AMS 580: Statistical Learning
  • AMS 560: Big Data System (Cloud Computing)
  • AMS 561: Introduction to Computational and Data Science
  • AMS 597: Statistical Computing
  • AMS 598: Big Data Analysis

Computer Science

1. Core Courses

  • CSE 505: Computing with Logic
  • CSE 512: Machine Learning
  • CSE 518: Foundations of Human Computer Interaction
  • CSE 519: Data Science Fundamentals
  • CSE 525: Introduction to Robotics
  • CSE 527: Introduction to Computer Vision
  • CSE 537: Artificial Intelligence
  • CSE 538: Natural Language Processing
  • CSE 544: Probability and Statistics for Data Scientists
  • CSE 545: Big Data Analytics
  • CSE 564: Visualization

2. Advanced Courses

  • CSE 612: Advanced Visualization
  • CSE 615: Advanced Computer Vision
  • CSE 628: Natural Language Processing

3. Seminars

  • CSE 646: Artificial intelligence
  • CSE 654: Visualization
  • CSE 656: Seminar in Natural Language Processing
  • CSE 657: Computer Vision

4. Special Topics

  • CSE 671: Artificial intelligence
  • CSE 679: Visualization
  • CSE 681: Computer Vision

Electrical Engineering

  • ESE 525: Modern Sensors in Artificial Intelligence Applications
  • ESE 533: Convex Optimization & Eng. Appl.
  • ESE 558: Digital Image Processing
  • ESE 561: Theory of Artificial Intelligence
  • ESE 562: AI Driven Smart Grids
  • ESE 563: Fundamentals of Robotics I
  • ESE 564: Artificial Intelligence for Robotics
  • ESE 568: Computer and Robot Vision
  • ESE 577: Deep Learning Algorithms and Software
  • ESE 587: Hardware Architectures for Deep Learning
  • ESE 588: Fundamentals of Machine Learning
  • ESE 589: Learning Systems for Eng. Appl.
  • ESE 590: Practical Machine Learning & Artificial Intelligence

Linguistics

  • LIN 537: Computational Linguistics I
  • LIN 637: Computational Linguistics 2
  • LIN 655: Computational Linguistics Seminar

Information Systems

  • ISE 503 Data Management

Mechanical Engineering

  • MEC 529: Introduction to Robotics
  • MEC 549: Robot Dynamics and Control
  • MEC 559: Mobile Robotics and Autonomous Vehicles
  • MEC 569: Aerial Robotics