Take Command of AI

Online Master’s in Artificial Intelligence Curriculum

AI expertise requires more than familiarity with individual tools. New York Tech’s 30-credit Online Artificial Intelligence, M.S. program delves into how intelligent systems work and how to evaluate and apply them responsibly.

Six required AI courses establish your technical foundation, while four electives let you pursue advanced areas that align with your interests.

Build Your Expertise From the Fundamentals Up

The curriculum develops the quantitative knowledge behind AI and builds on that foundation with increasingly sophisticated methods and applications. Along the way, you’ll consider the decisions and responsibilities involved in putting AI to work.

Strengthen Your Quantitative Foundation

The Optimization Methods for Data Science and Statistics courses establish the quantitative grounding you’ll use throughout the program. Together, they help you approach AI problems with a stronger understanding of how data, mathematical models, and uncertainty shape technical decisions.

Develop Core AI Knowledge

The courses in Machine Learning and Artificial Intelligence take you from quantitative foundations into the methods behind intelligent systems. You’ll learn how different approaches work, when they make sense for a problem, and how to evaluate the results they produce.

Build Responsible AI Judgment

Technical capability is only part of responsible AI development. The Responsible AI in Technology Fields course asks you to examine the consequences AI systems can create and make informed decisions about how those systems should be evaluated and deployed.

Online Artificial Intelligence Courses

The Online Artificial Intelligence, M.S. program consists of 18 credits of required coursework and 12 credits of electives. Review the course descriptions and prerequisites below.

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Required Courses (3 credits each)

DTSC 615: Optimization Methods for Data Science

Basic concepts in optimization are introduced. Linear optimization (linear and integer programming) will be introduced, including solution methods such as simplex and sensitivity analysis, with applications to transportation, network optimization, and task assignments. Unconstrained and constrained nonlinear optimization will be studied, and solution methods using tools such as Matlab/Excel will be discussed. Extensions to game theory and computational methods to solve static and dynamic games will be provided. Decision theory algorithms and statistical data analysis tools (Z-test, t-test, F-test, Bayesian algorithms, and Neyman-Pearson methods) will be studied. Linear and nonlinear regression techniques will be explored.

DTSC 620: Statistics for Data Sciences

This course presents a range of methods in descriptive statistics, frequentist statistics, Bayesian statistics, hypothesis testing, and regression analysis. Topics include point estimation, confidence interval estimation, nonparametric model estimation, parametric model estimation, Bayesian parametric models, Bayesian estimators, parametric testing, nonparametric testing, simple and multiple linear regression models, and logistic regression models.

CSCI 618: Responsible AI in Technology Fields

This graduate-level course explores the ethical, legal, social, and technical dimensions of responsible artificial intelligence (AI) across technology fields. Students examine fairness, transparency, accountability, privacy, safety, governance, and human-centered design in AI systems. The course integrates theoretical frameworks with real-world case studies from domains such as healthcare, finance, education, autonomous systems, and public policy. Students develop practical skills for designing, evaluating, and deploying AI systems responsibly.

DTSC 710: Machine Learning

Prerequisite: DTSC 615 — Optimization Methods for Data Science

In this course, students will learn important machine learning (ML) and data mining concepts and algorithms. Emphasis is on basic ideas and intuitions behind ML methods and their applications in activity recognition and anomaly detection. This course will cover core ML topics such as classification, clustering, feature selection, Bayesian networks, and feature extraction. Classroom teaching will be augmented with experiments performed on machine learning systems. Student understanding and progress will be measured through quizzes, exams, homework, project assignments, proposals, term-paper reports, and presentations.

CSCI 755: Artificial Intelligence

This course will cover machine learning (ML) concepts, decision theory, classification, clustering, feature selection, and feature extraction. Emphasis is on the core idea and optimization theory behind ML methods. Important ML applications (including biometrics and anomaly detection) will also be covered.

DTSC 870: Project I

In this course, students carry out independent research in a significant technical area of artificial intelligence. The student is to investigate a technical area, research it, advance it in some way if possible, and report on the learning and advancements made. A written report is required that summarizes the findings and any advancements made to the technology.

Elective Courses (3 credits each)

Choose four courses for a total of 12 credits.

CSCI 626: Information Retrieval

This course provides students with an introduction to the basics and techniques of information retrieval. Topics covered include search engines, retrieval strategies such as vector space, extended Boolean, and probabilistic models, and evaluation methods including relevance-based measures, query processing, indexing, and searching.

CSCI 636: Big Data Analytics

Organizations today are generating massive amounts of data that are too large and unstructured to fit in relational databases. Organizations and enterprises are turning to massively parallel computing solutions such as Hadoop. The Apache Hadoop platform allows for distributed processing of large data sets across clusters of computers using the MapReduce programming model. Students will gain an in-depth understanding of how MapReduce and Distributed File Systems work. In addition, they will be able to author Hadoop-based MapReduce applications in Java and use Hadoop subprojects Hive and Pig to build powerful data processing applications. Industry systems, such as IBM InfoSphere BigInsights and IBM InfoSphere Streams, will be studied.

CSCI 648: Natural Language Processing

This course introduces the theory and practice of natural language processing (NLP). Students study computational techniques for processing and understanding human language, including text representation, language modeling, syntactic and semantic analysis, information extraction, and neural approaches to NLP. Applications include machine translation, sentiment analysis, question answering, information retrieval, and conversational systems. Classroom instruction is complemented with hands-on programming assignments and projects using modern NLP frameworks.

CSCI 657: Introduction to Data Mining

This course introduces the concepts, techniques, and applications of data mining. Topics include data preprocessing, clustering, data warehouse and online analytical processing (OLAP) technology, cluster and social network analysis, data classification and prediction, multimedia, and web mining.

CSCI 728: Generative AI and Foundation Models

Prerequisite: DTSC 740 — Deep Learning

This course introduces students to the principles, architectures, and applications of generative artificial intelligence and foundation models. Topics include large language models, multimodal models, transformers, diffusion models, prompt engineering, fine-tuning, retrieval-augmented generation, evaluation techniques, and deployment considerations. Students explore the societal, ethical, and legal implications of generative AI technologies. Classroom instruction is complemented by hands-on projects using modern generative AI frameworks and platforms.

DTSC 740: Deep Learning

Prerequisites: DTSC 620 — Statistics for Data Sciences, DTSC 710 — Machine Learning

This course presents a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to problem domains such as speech recognition and computer vision.

CSCI 860: Special Topics

This course provides an opportunity for students to study, in a variety of formats, advanced topics that may not be included elsewhere in the curriculum. The topics may be of mutual interest to the student and faculty member or appropriate for group study.

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.

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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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