Stevens Institute of Technology - Graduate Studies Master in Artificial Intelligence
Stevens Institute of Technology - Graduate Studies

Stevens Institute of Technology - Graduate Studies

Master in Artificial Intelligence

Hoboken, USA

Master degree

2 years

English

Full time, Part time

USD 18,340 / per semester *

Distance Learning, On-Campus

* /semester Full-time Tuition Rate (9-12 credits)

Key Summary

    About : The Master in Artificial Intelligence offers in-depth knowledge and practical skills in AI technologies. The program focuses on areas like machine learning, robotics, and natural language processing. Creations of real-world projects help you gain valuable experience. The course can typically be completed in one to two years, depending on your study pace.
    Career Outcomes : Graduates can pursue roles such as AI engineers, data scientists, or research scientists across various industries including technology and healthcare. Opportunities also exist in developing innovative AI systems or working on cutting-edge research in academia and industry.

Overview

Stevens is traversing new frontiers in artificial intelligence by offering one of the first graduate programs in the country to explore AI applications for engineering.

In the artificial intelligence masters program, graduate students develop a strong background in the theoretical foundations and algorithm development in artificial intelligence and deep learning with a thorough understanding of a variety of engineering applications. Students will learn a blend of software and hardware skills that are applicable across multiple engineering domains.

Students are immersed in a hands-on approach to learning within a mix of course offerings, from fundamental core courses to state-of-the-art topics such as internet of things (IoT), information systems security, and big data applications.

Masters students in the applied artificial intelligence program have the opportunity to work on exciting new projects and applications relating to:

  • Machine learning
  • Deep learning
  • Image processing and computer vision
  • Autonomous robotics
  • Smart health
  • Biomedical engineering
  • Transportation engineering
  • Financial engineering
  • Embedded systems
  • Software engineering
  • Intelligent communications networks