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The Online Master’s in Artificial Intelligence

Master the Technology Driving the Future of AI

AI is transforming industries and redefining what’s possible. To move beyond using AI tools to understanding how they work, how intelligent solutions are built, and when their results can be trusted, you need a deeper level of expertise.

New York Tech’s Online Artificial Intelligence, M.S. program builds on your technical background with more intensive study of the principles and methods that drive AI technology. You’ll put that expanded knowledge to work as you build, evaluate, and responsibly deploy AI solutions.

You can complete the 30-credit program in as few as 10 months, with opportunities to study advanced areas such as deep learning, natural language processing, and generative AI.

Program Details:

  • 30 credits
  • 100% online
  • Complete in as few as 10 months
  • Flexible, asynchronous courses
  • Seven-week terms, with two courses per cycle for accelerated completion
  • Three opportunities to start per year

AI Expertise That Surpasses Today's Tools

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Today’s AI tools won’t be cutting-edge for long. That’s why the program focuses on more than learning how to use them.

You’ll study the principles behind AI systems and become adept at determining which approaches make sense for different problems. As you put those approaches into practice, you’ll learn to evaluate how well they work, recognize their limitations, and make informed decisions about how they should be used.

That combination of technical knowledge and judgment can help you adapt and solve new problems as AI evolves.

Build, Evaluate, and Apply Solutions

Your AI master’s degree coursework will give you opportunities to:

  • Define: Frame technical problems using data, statistics, and optimization
  • Build: Apply machine learning methods and explore neural networks, generative AI, large language models, and other AI technologies
  • Evaluate: Analyze performance, assess results, and identify limitations and risks
  • Apply: Put AI methods to work on practical problems
  • Advance: Conduct independent research, refine technical approaches, and deepen your expertise

Gain the Expertise to Shape AI

Get under the hood of intelligent technology. In New York Tech’s Online Artificial Intelligence, M.S. program, you’ll learn to build, evaluate, and responsibly apply intelligent systems, completing the 30-credit degree in as few as 10 months.

Fill out the form to access your program brochure and learn more about the curriculum, online experience, admissions, and career opportunities.

Advance from AI User to AI Innovator

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Develop a More Complete Command of AI

The 30-credit curriculum includes six required courses and four electives, combining foundational study with opportunities to explore advanced areas of AI.

Core courses cover optimization, statistics, machine learning, artificial intelligence, responsible AI, and independent project work. Through your electives, you can explore areas such as deep learning, natural language processing, information retrieval, data mining, and generative AI and foundation models.

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Build Responsible AI Judgment

Responsible AI in Technology Fields is part of the core curriculum. You’ll learn to recognize the risks and consequences AI systems can create and consider them throughout the process of evaluating and deploying those systems.

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Put Your AI Knowledge Into Action

The Project I course challenges you to conduct independent research into a significant technical area of AI. You’ll draw on what you’ve learned throughout the program to investigate the technology, pursue a question that interests you, and develop your own findings.

Course Spotlight

DTSC 710: Machine Learning

Study machine learning and data mining concepts and algorithms, with an emphasis on the ideas and intuitions behind machine learning methods. Explore classification, clustering, feature selection, Bayesian networks, and feature extraction, and apply your learning through experiments and project assignments.

CSCI 618: Responsible AI in Technology Fields

Examine the ethical, legal, social, and technical dimensions of artificial intelligence. Explore fairness, transparency, accountability, privacy, safety, governance, and human-centered design while developing practical skills for evaluating and deploying AI systems responsibly.

DTSC 740: Deep Learning

Explore neural networks, convolutional and recurrent network structures, deep unsupervised learning, reinforcement learning, and applications including speech recognition and computer vision.

CSCI 648: Natural Language Processing

Study the theory and practice of processing and understanding human language, including language modeling, syntactic and semantic analysis, information extraction, and neural approaches to natural language processing. Apply these concepts to areas such as machine translation, question answering, information retrieval, and conversational systems.

CSCI 728: Generative AI and Foundation Models

Explore the principles, architectures, and applications behind generative AI and foundation models. Topics include large language models, multimodal models, transformers, diffusion models, prompt engineering, fine-tuning, retrieval-augmented generation, evaluation, and deployment.
Review the Curriculum

Take On Bigger Challenges

Artificial intelligence is creating new opportunities for professionals who understand how intelligent systems work and how to build, evaluate, and apply them.

14x
Increase in share of hires, 2019–2025:1

AI engineers
22%
Job growth, 2025–2035:2

Computer and information research scientists
#1
Fastest-growing skills through 2030:3

AI and big data

That depth of knowledge can equip you for more complex AI work and greater technical responsibility as the field evolves.

Explore AI Careers

Admissions At A Glance

  • Completed application form
  • A bachelor’s degree from an accredited institution, with a GPA of 2.85 or higher on a 4.0 scale*

*Applicants with an undergraduate GPA of 2.5–2.84 may be considered for conditional admission at the program director’s discretion.

More Admissions Info
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Admissions Deadlines

Nov
13
Priority Deadline
November 13
Spring 2027
Jan
11
Final Deadline
January 11
Spring 2027
Jan
25
Start Date
January 25
Spring 2027

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Sources:
  1. Retrieved on October 2, 2026, from economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/us-software-engineer-talent-landscape-2026.pdf
  2. Retrieved on October 2, 2026, from bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
  3. Retrieved on October 2, 2026, from weforum.org/publications/the-future-of-jobs-report-2025/