Curriculum Requirements
Master of Science in Artificial Intelligence
Major Requirements
| Required Courses | Credits: | |
| DTSC 615 | Optimization Methods for Data Science | 3 |
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Prerequisite: Corequisites: DTSC 635 Basic concepts in optimization are introduced. Linear optimization (linear and integer programming) will be introduced including solution methods like simplex and the sensitivity analysis with applications to transportation, network optimization and task assignments. Unconstrained and constrained non-linear optimization will be studied and solution methods using tools like Matlab/Excel will be discussed. Extensions to game theory and computational methods to solve static, 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 non-linear regression techniques will be explored. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 620 | Statistics for Data Science | 3 |
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This course presents a range of methods in descriptive statistics, frequentist statistics, Bayesian statistics, hypothesis testing, and regression analysis. Topics includes 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, logistic regression model. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| CSCI 618 | Responsible AI in Technology Fields | 3 |
| Please view all course descriptions: http://www.nyit.edu/courses | ||
| DTSC 710 | Machine Learning | 3 |
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Prerequisite: Prerequisite: DTSC 615 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 assii.mments, proposals, term-paper reports, and presentations. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| CSCI 755 | Artificial Intelligence I | 3 |
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Prerequisite: Prerequisite: CSCI 651 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 870 | Project I | 3 |
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In this course students carry out independent research in a significant technical area of data science. 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| Total: 18 Credits | ||
| Electives (select four courses from the following) | Credits: | |
| CSCI 626 | Information Retrieval | 3 |
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This course provides students with an introduction to the basics and techniques of information retrieval. Topics cover search engines, retrieval strategies such as vector space, extended Boolean, probabilistic models and evaluation methods including relevance-based measures, query processing, indexing and searching. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| CSCI 636 | Big Data Analytics | 3 |
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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 map and reduce 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. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| CSCI 648 | Natural Language Processing | 3 |
| Please view all course descriptions: http://www.nyit.edu/courses | ||
| CSCI 657 | Introduction to Data Mining | 3 |
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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. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| DTSC 740 | Deep Learning | 3 |
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Prerequisite: Prerequisites: DTSC 620, DTSC 710 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 like speech recognition and computervision. Classroom Hours- Laboratory and/or Studio Hours- Course Credits: 3-0-3 Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| CSCI 728 | Generative AI and Foundation Models | 3 |
| Please view all course descriptions: http://www.nyit.edu/courses | ||
| CSCI 860 | Special Topics | 3 |
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This course provides an opportunity for students to study, in a variety of formats, advanced topics which 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. Classroom Hours - Laboratory and/or Studio Hours – Course Credits: 3-0-3 |
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| Total: 12 Credits | ||
| Total Required Credits = 30 |