Artificial Intelligence (Short Course)

Non-degree

Starts: 15 Dec 2025

Est. completion in 3 days

Offered through In-person offering at sponsor site

Avg. tuition cost per course: See tuition Info For specific tuition costs of each program or contact information, please contact the NPS Tuition office at tuition@nps.edu .

Learn more about Service Obligation Info Officers accepting orders to a Graduate Education Program (GEP) are obligated to serve on active duty after completion.

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Overview

This short course provides a high-level introduction to the core ideas of artificial intelligence (AI), with an orientation towards defense applications and a focus on the machine-learning subarea. This includes an introduction to numerical models like neural networks, logical models like generative AI, and models that do planning. It also includes key issues in suitability of projects for AI, testing of AI methods, and successful management of AI projects.

 

 

Dr. Neil Rowe

Professor of Computer Science at the Naval Postgraduate School where he has been since 1983. He has a Ph.D. in Computer Science from Stanford University and three degrees from MIT. His main research interests are artificial intelligence, processing of big data, the modeling of deception, information security, and digital forensics. He is the author of a book on artificial intelligence, a book on cyberdeception, and 220 refereed technical papers. Currently he heads the Computer Science Ph.D. Program Committee and the Computer Science specialization track in artificial intelligence, and is program director for the Artificial Intelligence for Military Use Certificate. Prof. Rowe was rated in the top 2% of computer scientists through 2022.

Course Cost: Per participant, contact online@nps.edu for details.

Audience: Mid-grade or senior officers

Learning Outcomes

After successfully completing this short course, you will:

  1. Be able to identify what tasks AI is good for.
  2. Be able to explain and compare standard AI models for useful tasks.
  3. Be able to explain common methods of machine learning for optimizing AI models.
  4. Be able to identify key steps and pitfalls in implementing AI projects.