Artificial Intelligence Master of Science Degree

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The artificial intelligence master’s will teach you to harness the benefits of AI and gain transferable skills in the responsible and impactful design, development, analysis, and deployment of artificial intelligence.

Overview for Artificial Intelligence MS

Why study AI at RIT?

  • STEM-OPT Visa Eligible: The STEM Optional Practical Training (OPT) program allows full-time, on-campus international students on an F-1 student visa to stay and work in the U.S. for up to three years after graduation.
  • Flexible Learning: Complete your degree entirely online, or via a combination of online and traditional on-campus courses.
  • AWARE-AI Program: MS in AI students have the opportunity to become National Science Foundation’s AWARE-AI trainees and experience AI research carefully curated with career-enhancing activities. 

 

There is an enormous and growing demand for AI professionals across all sectors of society. This artificial intelligence master’s degree is designed for students with an interest in various AI sectors from various educational backgrounds.

You will develop well-rounded skill-sets in designing, developing, and deploying AI systems, and also in understanding and analyzing AI’s impact on policy and society. A rich set of core courses prepare you with essential technical skills and knowledge.

Master’s in Artificial Intelligence

RIT’s artificial intelligence master’s offers you a tailored experience through your choice of electives. For example, you can study a central AI topic or an impactful domain of AI applications. You will gain career-enhancing experience through hands-on projects and course work. Prior to graduation, a capstone or an optional thesis allows you apply learned skills to evaluate or investigate an active area in artificial intelligence.

AI Course Work

  • Core courses: You will develop a range of essential AI skills and knowledge through core courses. If necessary, there are computer programming and a mathematical bridge courses available.

  • Elective courses: Make this degree your own by customizing electives to fit your goals. Develop depth in an area of special interest with electives that focus on central AI themes such as machine learning, natural language and speech processing, computer vision, robotics, sociotechnical AI analysis, and more.

  • Capstone or thesis: Choose to complete a capstone course and an extra elective course, or spend the equivalent of two courses on a thesis project with an individual expert advisor.

Interdisciplinary Curriculum

The master’s in AI is jointly delivered by faculty experts from four RIT colleges–Golisano College of Computing and Information Sciences, College of Liberal Arts, College of Science, and Kate Gleason College of Engineering–allowing you to grow valuable, career-enhancing interdisciplinary skills and communication competency as part of your program experience.

Careers in Artificial Intelligence

Graduates of the master's in artificial intelligence are equipped with the tools and knowledge for successful careers in industry or other organizations. They will also be prepared for doctoral degree programs in a range of areas, as the impact of AI expands into established and emerging career professions.
 

This program is offered on-campus or online.

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    30% Tuition Scholarship for NY Residents and Graduates

    Now is the perfect time to earn your Master’s degree. If you’re a New York state resident with a bachelor’s degree or have/will graduate from a college or university in New York state, you are eligible to receive a 30% tuition scholarship.

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Careers and Experiential Learning

Typical Job Titles

AI Engineer Machine Learning Specialist Software Developer
Entrepreneur Research Associate AI Policy Specialist
Technology Analyst Computational Linguist

Cooperative Education

What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.

Cooperative education is optional but strongly encouraged for graduate students in the artificial intelligence master's degree.

Creative Industry Day

RIT’s Office of Career Services and Cooperative Education hosts Creative Industry Day, which connects students majoring in art, design, film and animation, photography, and select computing majors with companies, organizations, creative agencies, design firms, and more. You'll be able to network with company representatives and interview directly for open co-op and permanent employment positions.

Featured Work and Profiles

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Curriculum for 2024-2025 for Artificial Intelligence MS

Current Students: See Curriculum Requirements

View Printable Curriculum

Artificial Intelligence, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
IDAI-610
Fundamentals of Artificial Intelligence
This course covers the underlying theories and algorithms used in the field of artificial intelligence. Topics include the history of AI, search algorithms (such as A*, game search and constraint satisfaction), logic and logic programming, planning, and an overview of machine learning. Programming assignments, including implementation of AI algorithms, and oral/written summaries of research papers are required. Lecture 3 (Fall).
 3
IDAI-620
Mathematical Methods for Artificial Intelligence
This course introduces the mathematical background necessary to understand, design, and effectively deploy AI systems. It focuses on four key areas of mathematics: (1) linear algebra, which enables describing, storing, analyzing and manipulating large-scale data; (2) optimization theory, which provides a framework for training AI systems; (3) probability and statistics, which underpin many machine learning algorithms and systems; and (4) numerical analysis, which illuminates the behavior of mathematical and statistical algorithms when implemented on computers. Lecture 3 (Fall).
 3
IDAI-700
Ethics of Artificial Intelligence
This course will familiarize students with foundational concepts and emerging ideas in the ethics of artificial intelligence and their implications for public policy. It will be broken down into three sections: (1) the ethics of machine learning; (2) the moral status of AI; and (3) AI and the distant future. The first section will consider such topics as the ethical implications of unconscious bias in machine learning (e.g., in predictive text, facial recognition, speech dialogue systems); what constraints should govern the behavior of autonomous and semi-autonomous machines such as drones and smart cars; whether AI can undermine valuable social institutions and perhaps to democracy itself and what might be done to mitigate such risk; and how automation might transform the labor economy and whether this morally desirable. The second section turns to the question of our moral obligations toward (some) artificial intelligences. Here, we will ask what grounds moral status in general and how this might apply to artificial intelligences in particular, including how should we should balance moral obligations toward (some) AIs with competing obligations toward human beings and other creatures with morally protectable interests. The final section will look to the far distant future and consider how (if at all) we might identify and estimate future threats from AI and what might be done today to protect all those who matter morally. Lecture 3 (Fall).
 3
IDAI-710
Fundamentals of Machine Learning
This course is an introduction to machine learning theories and algorithms. Topics include an overview of data collection, sampling and visualization techniques, supervised and unsupervised learning and graphical models. Specific techniques that may be covered include classification (e.g., support vector machines, tree-based models, neural networks), regression, model selection and some deep learning techniques. Programming assignments and oral/written summaries of research papers are required. (Prerequisites: IDAI-610 and IDAI-620 or equivalent courses.) Lecture 3 (Spring).
 3
IDAI-720
Research Methods for Artificial Intelligence
Hallmarks of AI are systems that perform human-like behaviors, and AI systems rely on continuous preparation and deployment of data resources as new tasks emerge. In this course, students develop their conceptual, applied, and critical understanding about (1) experimental principles and methods guiding the collection, validation, and deployment of human data resources for AI systems; (2) human-centered AI concepts and techniques including dataset bias, debiasing, AI fairness, humans-in-the loop methods, explainable AI, trust), and (3) best practices for technical writing and presentation about AI. As a milestone, based on research review, students will write and present an experimental design proposal for dataset elicitation followed by computational experimentation, with description and visualization of the intended experiment setup, as well as critical reflection of benefits, limitations, and implications in the context of AI system development and deployment. (Prerequisites: IDAI-610 and IDAI-700 or equivalent courses.) Lecture 3 (Spring).
 3
 
Program Elective
 3
Second Year
Choose one of the following tracks:
6
   IDAI-780
Capstone Project, plus one additional Program Elective
Graduate capstone project by the candidate on an appropriate topic as arranged between the candidate and the research advisor. Lecture 3 (Fall, Spring, Summer).
  
   IDAI-790
Research and Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. Thesis (Fall, Spring, Summer).
  
 
Program Electives
 6
Total Semester Credit Hours
30

* IDAI-699 Graduate Co-op: A co-op is entirely optional at the graduate level, with permission of the school director, and may delay time to completion depending on scheduling constraints. Co-op experiences are zero credit.

MS Program Electives

Machine Learning

Electives
CISC-863
Statistical Machine Learning*
This course will cover the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian, maximizing a posteriori (MAP), and maximum likelihood (ML) parameter estimation, regularization and sparsity-promoting priors, kernel methods, adaptive basis function methods, the expectation maximization algorithm, Monte Carlo methods, variational methods, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples. Each student will complete several problem sets, including both mathematical and computer implementation problems. Probability and Statistics I, Linear Algebra, and Introduction to Computer Programming. Familiarity with a numerical mathematics package (e.g. Matlab, Maple, Mathematica) is helpful but not required. (This course is restricted to students with graduate standing in GCCIS, KGCOE, or COS.) Lecture 3 (Spring).
CMPE-679
Deep Learning†
Deep learning has been revolutionizing the fields of object detection, classification, speech recognition, natural language processing, action recognition, scene understanding, and general pattern recognition. In some cases, results are on par with and even surpass the abilities of humans. Activity in this space is pervasive, ranging from academic institutions to small startups to large corporations. This course emphasizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs), but additionally covers reinforcement learning and generative adversarial networks. In addition to achieving a comprehensive theoretical understanding, students will understand current state-of-the-art methods, and get hands-on experience at training custom models using popular deep learning frameworks. (Prerequisites: CMPE-677 or equivalent course and students in CMPE-BS or CMPE-MS programs.) Lecture 3 (Spring).
CISC-865
Deep Learning†
Deep learning represents a set of emerging techniques in machine learning that has quickly become prevalent in the analysis of big data. The power and potential of this recent breakthrough in intelligent computer systems has been demonstrated through many successes. Deep learning systems are the current best performer in computer vision and speech processing. A wide variety of active researches are being conducted to leverage the capability of deep learning for achieving automation in areas such as autonomous driving, robotics, and automated medical diagnosis. There is a crucial need to educate our students on such new tools. This course gives an in-depth coverage of the advanced theories and methods in deep learning including basic feedforward neural networks, convolutional neural networks, recurrent neural networks including long short-term memory models, deep belief nets, and autoencoders. It will make an emphasis on approaches with practical relevance, and discusses a number of recent applications of deep networks applications in computer vision, natural language processing and reinforcement learning. (Prerequisites: CISC-863 or equivalent course.) Lecture 3 (Fall).
CSCI-736
Neural Networks and Machine Learning
The course will introduce students into the current state of artificial neural networks. It will review different application areas such as intrusion detection and monitoring systems, pattern recognition, access control and biological authentication, and their design. The students will be required to conduct research and analysis of existing applications and tools as well as to implement a course programming project on design of a specified application based on neural networks and/or fuzzy rules systems. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lec/Lab 3 (Spring).
CSEC-720
Deep Learning Security
This course covers the intersection of cybersecurity and deep learning technologies such as CNNs, LSTMs, GANs, and Transformers. Topics include the application of deep learning to traffic analysis, deepfake detection, malware classification, and fooling deep learning classifiers with adversarial examples. Students will present research papers, perform several exercises to apply attack and defense techniques, and complete a final research project. Prior experience with machine learning concepts and implementation is required, but necessary details on deep learning will be covered. (Prerequisites: CSEC-620 or CSCI-630 or CSCI-631 or CSCI-635 or CMPE-677 or IDAI-710 or equivalent course.) Lecture 3 (Spring).
DSCI-640
Neural Networks
This course will cover modern and deep neural networks with a focus on how they can be correctly implemented and applied to a wide range of data types. It will cover the backpropagation algorithm and how it is used and extended for deep feedforward, recurrent and convolutional neural networks. An emphasis will be placed on the implementation, design, testing and training of neural networks. The course will also include an introduction to using a modern neural network framework. (Prerequisites: SWEN-601 or equivalent course.) Lec/Lab 3 (Spring).
ISEE-601
Systems Modeling and Optimization
An introductory course in operations research focusing on modeling and optimization techniques used in solving problems encountered in industrial and service systems. Topics include deterministic and stochastic modeling methodologies (e.g., linear and integer programming, Markov chains, and queuing models) in addition to decision analysis and optimization tools. These techniques will be applied to application areas such as production systems, supply chains, logistics, scheduling, healthcare, and service systems. Note: Students required to take ISEE-301 for credit may not take ISEE-601 for credit. (This course is restricted to students in ISEE-MS, ENGMGT-MS, MIE-PHD, AI-MS, or BIME-BS students with a BIMEISEE-U subplan.) Lecture 3 (Fall).
ISEE-701
Linear Programming
Computational techniques for solving constrained optimization problems. Linear programming, the Simplex method and variations, duality and sensitivity testing. (Prerequisite: ISEE-301 or ISEE-601 or IDAI-620 or equivalent course.) Lecture 3 (Spring).
ISEE-702
Integer and Nonlinear Programming
An introduction to the mathematical foundations of integer programming and nonlinear optimization techniques. Study of algorithms and computer-aided solutions for applied optimization problems. (Prerequisite: ISEE-301 or ISEE-601 or IDAI-620 or equivalent course.) Lecture 3 .
ISEE-761
Forecasting Methods
Forecasting Methods will provide the engineering student with the skills necessary to perform data driven time series analysis from an engineering applications perspective. A process driven approach will be used covering the entire forecasting process from data preparation and pre-processing techniques to model selection, performance evaluation, and monitoring. A special emphasis will be placed on performance evaluation and improvement of models used to predict RIT energy demand and peak load days. The course will cover topics in data cleansing, data transformation, trend and seasonality analysis, smoothing techniques, regression analysis for forecasting, seasonal and non-seasonal ARIMA models, dynamic regression, neural networks and advanced modeling techniques for multivariate time series analysis. Lectures and assignments will focus on predicting RIT energy demand considering circuits with 2MW solar fields or similar data sets. (Prerequisites: ISEE-561 or ISEE-661 or equivalent course.) Lecture 3 (Biannual).
MECE-689
Grad.Lower Level Special Topic: Reinforcement Learning
Topics and subject areas that are not regularly offered are provided under this course. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor. Lecture (Fall, Summer).
STAT-747
Principles of Statistical Data Mining*
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611, STAT-731 and STAT-741 or equivalent courses.) Lecture 3 (Fall, Spring).

Natural Language and Speech Processing

Electives
PSYC-681
Natural Language Processing I
This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Machine learning, including standard and recent neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools. Expected: Programming skills, demonstrated by coursework or instructor approval. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).
PSYC-682
Natural Language Processing II
Study of a focus area of increased complexity in natural language processing. The focus varies each semester. Students will develop skills in computational linguistics analysis in a laboratory setting, according to professional standards. A research project plays a central role in the course. Students will engage with relevant research literature, research design and methodology, project development, and reporting in various formats. (Prerequisite: PSYC-681 or (IDAI-610 and IDAI-620) or equivalent courses.) Lecture 3 (Spring).
PSYC-684
Graduate Speech Processing
This course introduces students to speech and spoken language processing with a focus on real-world applications including automatic speech recognition, speech synthesis, and spoken dialog systems, as well as tasks such as emotion detection and speaker identification. Students will learn the fundamentals of signal processing for speech and explore the theoretical foundations of how human speech can be processed by computers. Students will then collect data and use existing toolkits to build their own speech recognition or speech synthesis system. This course provides theoretical foundation as well as hands-on laboratory practice. Expected: Programming skills, demonstrated by coursework or instructor approval. Lecture 3 (Fall).
PSYC-712
Graduate Cognition*
This course will survey theoretical and empirical approaches to understanding the nature of the mental processes involved in attention, object recognition, learning and memory, reasoning, problem solving, decision-making, and language. The course presents a balance between historically significant findings and current state of-the-art research. Readings that have structured the nature and direction of scientific debate in these fields will be discussed. The course also includes discussions of methodology and practical applications. Students will have opportunities to develop their research skills and critical thinking by designing research studies in cognitive psychology. Seminar (Spring).

Neuromorphic Computing

Electives
CMPE-755
High Performance Architectures
This course will focus on learning and understanding the available hardware options to satisfy the needs of high performance and computational intensive applications. Special attention will be paid to single platform massively parallel devices, their programming and efficient use of the hardware resources. The course will include hands on work with the actual device, lab work, and technical reports and conference paper reading as a relevant source information. (Prerequisite: CMPE-350 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Fall).
CMPE-789
Special Topics (Topic ID #30 Neuromorphic Computing)
Graduate level topics and subject areas that are not among the courses typically offered are provided under the title of Special Topics. Such courses are offered in a normal format; that is, regularly scheduled class sessions with an instructor. (This class is restricted to students in the CMPE-BS, CMPE-MS or CMPE-BS/MS programs.) Lecture 3 (Fall, Spring).
COGS-610
Laboratory Methods
Scientists use a wide range of experimental methods to elucidate the function of the human brain and mind. This course will provide an overview of these methods, in order to allow students to understand a wide range of scientific studies and to be able to select an appropriate method for a specific research topic. Such methods include neuroimaging, psychophysiology, single-cell recordings, computational modeling, and cognitive psychology and behavioral methods that use measures such as response time and decision accuracy to test theories concerning the nature of mental processes and representations. Lecture 3 (Fall).
COGS-760
Foundations of Cognitive Modeling
This course will introduce students to the mathematical and philosophical foundations of cognitive modeling as well as the key concepts and tools needed for developing and applying cognitive architectures. Furthermore, the course will survey seminal papers as well as leading computational frameworks used in understanding human cognition and intelligence.Topics will include fundamentals of signal detection theory, probability modeling and information theory, the Lens Model, statistical (Bayesian) modeling of various cognitive actions and behavior, dynamical systems, symbolic and sub-symbolic representations, and simulation using artificial neural networks. Students will learn how to use one or more major cognitive architectures, e.g., MicroSAINT, Act-R, Soar, Nengo, and build basic computational models of cognitive processes, including those related to categorization, language, memory, decision making, and reasoning, fitting and evaluating their models to different kinds of behavioral data. Lecture 3 (Biannual).
CSCI-633
Biologically Inspired Intelligent Systems
There have been significant advances in recent years in the areas of neuroscience, cognitive science and physiology related to how humans process information. In this course students will focus on developing computational models that are biologically inspired to solve complex problems. A research paper and programming project on a relevant topic will be required. A background in biology is not required. (CSCI-603,605,661 or CSCI ETC..) Lecture 3 (Fall).
CSCI-722
Data Analytics Cognitive Comp*
Building on prior knowledge of data analytics, this course brings in the impact of natural language processing and cognitive computing on data analysis. Topics include an overview of natural language processing; data mining, information retrieval and knowledge processing; corpus identification and preparation; training and test data and methods; current research in the field; and ethical concerns. Students will apply the concepts learned in class through team projects, programming assignments, presentations, and a research paper. (Prerequisites: CSCI-620 or (CSCI-420 and CSCI-320) or (4003-485 and 4003-487) or equivalent course.) Lecture 3 (Fall).

Robotics

Electives
CSCI-632
Mobile Robot Programming
This course covers standard and novel techniques for mobile robot programming, including software architectures, reactive motion control, map building, localization and path planning. Other topics may include multiple robot systems, robot vision and non-traditional and dynamic robots. Students will implement various algorithms in simulation as well as on a real robot, and investigate and report on current research in the area. Course offered every other year. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lecture 3 (Spring).
EEEE-636
BioRobotics/Cybernetics
Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 2, Lecture 3 (Spring).
EEEE-685
Principles of Robotics
An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 3, Lecture 3 (Fall).
EEEE-784
Advanced Robotics
This course explores advance topics in mobile robots and manipulators. Mobile robot navigation, path planning, room mapping, autonomous navigation are the main mobile robot topics. In addition, dynamic analysis of manipulators, forces and trajectory planning of manipulators, and novel methods for inverse kinematics and control of manipulators will also be explored. The pre-requisite for this course is Principles of Robotics. However, students would have better understanding of the topics if they had Control Systems and Mechatronics courses as well. The course will be a project based course requiring exploration of a novel area in Robotics and writing an IEEE conference level paper. (Prerequisites: EEEE-585 or EEEE-685 or equivalent course.) Lab 2, Lecture 3 (Spring).

Sociotechnical Analytics and Policy of Artificial Intelligence

Electives
COMM-717
Artificial Intelligence and Communication
Communication has been impacted by automation and advances in information technology, and now artificial intelligence is changing how we interact with socio-technical systems. In this course, we will explore historical, ethical, computational, and cultural perspectives to understand the implications of algorithmic processes on communication and society. During the course, students will learn how to analyze various digital products and identify the potential consequences of algorithmic systems on various demographics. Lecture 3 (Fall or Spring).
DSCI-633
Foundations of Data Science and Analytics
A foundations course in data science, emphasizing both concepts and techniques. The course provides an overview of data analysis tasks and the associated challenges, spanning data preprocessing, model building, model evaluation, and visualization. The major areas of machine learning, such as unsupervised, semi-supervised and supervised learning are covered by data analysis techniques including classification, clustering, association analysis, anomaly detection, and statistical testing. The course includes a series of assignments utilizing practical datasets from diverse application domains, which are designed to reinforce the concepts and techniques covered in lectures. A substantial project related to one or more data sets culminates the course. (This course is restricted to DATASCI-MS, INFOST-MS, SOFTENG-MS, COMPSCI-MS, or COMPIS-PHD Major students.) Lecture 3 (Fall, Spring).
ISTE-782
Visual Analytics*
This course introduces students to Visual Analytics, or the science of analytical reasoning facilitated by interactive visual interfaces. Course lectures, reading assignments, and practical lab experiences will cover a mix of theoretical and technical Visual Analytics topics. Topics include analytical reasoning, human cognition and perception of visual information, visual representation and interaction technologies, data representation and transformation, production, presentation, and dissemination of analytic process results, and Visual Analytic case studies and applications. Furthermore, students will learn relevant Visual Analytics research trends such as Space, Time, and Multivariate Analytics and Extreme Scale Visual Analytics. Lec/Lab 3 (Spring).
MGIS-650
Introduction to Data Analytics and Business Intelligence*
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments. Lecture 3 (Fall).
PSYC-712
Graduate Cognition
This course will survey theoretical and empirical approaches to understanding the nature of the mental processes involved in attention, object recognition, learning and memory, reasoning, problem solving, decision-making, and language. The course presents a balance between historically significant findings and current state of-the-art research. Readings that have structured the nature and direction of scientific debate in these fields will be discussed. The course also includes discussions of methodology and practical applications. Students will have opportunities to develop their research skills and critical thinking by designing research studies in cognitive psychology. Seminar (Spring).
PSYC-714
Graduate Engineering Psychology
In this course the students will learn to recognize the integrated (systems) nature of Engineering Psychology, the centrality of human beings in systems design, and to use the topics covered and the available knowledge base to adapt the environment to people. This course will cover several fundamental models of human information processing in the context of human-system interactions. The models may include but are not limited to Signal Detection Theory, Information Theory, theories of attention, both normative and naturalistic decision-making models, Control Theory, and the Lens Model of Brunswick, as well as models of the human as a physical engine, that is, anthropometry, biomechanics, and work physiology. Most topics include readings in addition to the course text as well as a lab exercise with a detailed lab report. Seminar (Biannual).
PSYC-719
Human Factors in Artificial Intelligence
This course will provide students with fundamental information for human-centered design of applications of artificial intelligence. There are three parts to the course: The first part is about methods of design and evaluation. The second part introduces students to the psychology of sensation and perception, memory, attention, judgment, decision-making, and problem solving, as well as human error and reliability. Finally, students will become familiar with design principles as they apply to displays and controls, human-computer interaction, human-automation interaction, and human-centered automation. Guest lectures and case studies will be examined to illustrate topics covered in it and to provide a survey of the current state of AI research, development, and controversies. Ethics and moral responsibility in technology development, with links to current policy debates, are also discussed in this context. Lecture 1 (Fall).
PUBL-610
Technological Innovation and Public Policy*
Technological innovation, the incremental and revolutionary improvements in technology, has been a major driver in economic, social, military, and political change. This course will introduce generic models of innovation that span multiple sectors including: energy, environment, health, and bio- and information-technologies. The course will then analyze how governments choose policies, such as patents, to spur and shape innovation and its impacts on the economy and society. Students will be introduced to a global perspective on innovation policy including economic competitiveness, technology transfer and appropriate technology. Lecture 3 (Spring).
PUBL-650
AI, Policy and Law
Artificial intelligence (AI) presents many complex issues for society, as technological developments have greatly outpaced public policy. Moreover, the open and commercialized nature of AI tools provides criminals and other adversarial actors with new advantages yet to be effectively countered. This class looks at the legal and policy frameworks and practices needed to build an ecosystem of privacy, security, and trust that will help ensure stakeholders that AI is being developed and deployed in an ethical, safe, and reliable manner. The class will also discuss how organizations are designing their own practices for operationalizing trustworthy or ethical AI in various sectors including law enforcement and criminal justice, commercial sectors, medical and biological research, among others. Students will be given a foundation in the emerging laws, regulations, and policies regarding AI, as well as insight on the broader process of how laws and policies need to adapt for other rapidly emerging technologies. We will explore in detail several approaches currently being considered, including regulatory approaches, standards, and considerations for national and international security. The course also will explore certain other legal issues arising in connection with AI, such antitrust and competition law, intellectual property and proprietary rights matters, and concerns for future technologies (quantum computing, AI and synthetic biology, etc.). Lecture 3 (Fall, Spring).

Vision

Electives
CMPE-685
Computer Vision
This course covers both fundamental concepts and the more advanced topics in Computer Vision. Topics include image formation, color, texture and shape analysis, linear filtering, edge detection and segmentation. In addition, students are introduced to more advanced topics, such as model based vision, object recognition, digital image libraries and applications. Homework, literature reviews and programming projects are integrated with lectures to provide a comprehensive learning experience. (Prerequisites: CMPE-480 or equivalent course or graduate standing in the CMPE-MS program.) Lecture 3 (Spring).
CSCI-731
Advanced Computer Vision*
This course examines advanced topics in computer vision including motion analysis, video processing and model based object recognition. The topics will be studied with reference to specific applications, for example video interpretation, robot control, road traffic monitoring, and industrial inspection. A research paper, an advanced programming project, and a presentation will be required. (Prerequisites: CSCI-631 or CSCI-431 or equivalent course.) Lecture 3 (Spring).
CSCI-732
Image Understanding
This course explores the theory and methodologies used to interpret images in terms of semantic content. Techniques from image processing and pattern recognition are extended for the purpose of scene understanding using both a bottom-up and a top-down approach. Topics include human visual perception, knowledge representation, object recognition, contextual classification, scene labeling, constraint propagation, interpretation trees, semantic image segmentation, 3D models and matching, active vision, and reasoning about images. Programming projects are required. Offered every other year. (Prerequisites: CSCI-631 or CSCI-431 or equivalent course.) Lecture 3 (Spring).
CSCI-736
Neural Networks and Machine Learning
The course will introduce students into the current state of artificial neural networks. It will review different application areas such as intrusion detection and monitoring systems, pattern recognition, access control and biological authentication, and their design. The students will be required to conduct research and analysis of existing applications and tools as well as to implement a course programming project on design of a specified application based on neural networks and/or fuzzy rules systems. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lec/Lab 3 (Spring).
EEEE-670
Pattern Recognition
This course provides a rigorous introduction to the principles and applications of pattern recognition. The topics covered include maximum likelihood, maximum a posteriori probability, Bayesian decision theory, nearest-neighbor techniques, linear discriminant functions, and clustering. Parameter estimation and supervised learning as well as principles of feature selection, generation and extraction techniques, and utilization of neural nets are included. Applications to face recognition, classification, segmentation, etc. are discussed throughout the course. (Prerequisites: EEEE-602 and EEEE-707 and EEEE-709 or equivalent courses.) Lecture 3 (Spring).
IMGS-612
Computer Vision
IMGS-682
Image Processing and Computer Vision
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus. Lecture 3 (Spring).
IMGS-712
Multi-view Imaging
Images are 2D projections gathered from scenes by perspective projection. By making use of multiple images it is possible to construct 3D models of the scene geometry and of objects in the scene. The ability to derive representations of 3D scenes from 2D observations is a fundamental requirement for applications in robotics, intelligence, medicine and computer graphics. This course develops the mathematical and computational approaches to modeling of 3D scenes from multiple 2D views. After completion of this course students are prepared to use the techniques in independent research. (Prerequisites: IMGS-616 or IMGS-682 or equivalent course.) Lecture 3 (Spring).
IMGS-789
Graduate Special Topics (Topic ID #10 Deep Learning for Vision)
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
IMGS-789
Graduate Special Topics (Topic ID #19 Robust ML Interdisciplinary Imaging Science App)
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
PSYC-715
Graduate Perception
The course is designed to provide students with a deeper understanding of topics in perception. This course will be organized such that students will work in groups on various projects as well as covering topics through readings and classroom discussion. The topics may include, but are not limited to: spatial frequency perception; aftereffects, visual illusions and their relationship to cortical function and pattern perception; color perception; depth and motion perception; higher order perception such as face and object recognition; and music and speech perception. The goal is to cover current research and theories in perception, looking at current developments and their antecedents. The course will be divided into various modules. Students will be assigned readings relevant to each section of the course, and will be expected to master the major concepts. Group discussion of the readings will complement lectures where the instructor will present relevant background material. There will also be laboratory time for the students, where they will examine empirical findings in perception, and develop their research skills in the field. Lecture 3 (Biannual).

Other

Electives
DSCI-650
High Performance Data Science
This course will cover concurrent, parallel and distributed programming paradigms and methodologies with a focus on implementing them for use in applied data science or scientific computing tasks. In particular, the course will focus on developing software using graphical processing units (GPUs) and the message passing interface (MPI); with an emphasis on properly handling large-scale, real-world data as part of these applications. The course will also teach scalability and load balancing techniques for developing efficient distributed systems. Programming assignments are required. (Graduate Computing and Information Sciences) Lecture 3 (Fall).
SWEN-601 
Software Construction*
This is a programming based course to enhance individual, technical engineering knowledge and skills as preparation for masters level graduate work in computing. Students will be introduced to programming language syntax, object oriented concepts, data structures and foundational algorithms. An emphasis will be placed on obtaining practical programming skills, through regular programming assignments and practicum. (Corequisites: SWEN-610 and SWEN-746 or equivalent courses.) Lecture 3 (Fall).
SWEN-711
Engineering Self-Adaptive Software Systems With Reinforcement Learning
This course introduces beginning graduate students to key concepts and techniques underlying the engineering of self-adaptive and autonomic software systems. Such software systems are capable of self-management, self-healing, self-tuning, self-configuration and self-protection. The course content includes an introduction of self-adaptive software systems and defines their characteristics. This will be followed by foundational engineering principles and methodology for achieving self-adaptive systems – feedback control, modeling, machine learning, and systems concepts. Selected seminal research paper reading and a term-long project will also be covered in the class. (Prerequisites: (SOFTENG-U or CSCISWEN-U or SOFTENG-MS students) and ((SWEN-601 and SWEN-610) or (SWEN-261) or equivalent courses).) Lecture 3 (Fall).
IDAI-799
Independent Study in Artificial Intelligence
The student will work independently, under the supervision of one or more faculty members, on a topic that either expands beyond the depth of or addresses content not covered in another IDAI course. (Permission of instructor and joint program director.) (Prerequisites: IDAI-610 and IDAI-620 and IDAI-700 or equivalent courses.) Ind Study (Fa/sp/su).

* These elective courses have recently been offered online.

† AI-MS students may choose either CISC-865 Deep Learning or CMPE-679 Deep Learning.

Note for online students

The frequency of required and elective course offerings in the online program will vary, semester by semester, and will not always match the information presented here. Online students are advised to seek guidance from the listed program contact when developing their individual program course schedule.

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Admissions and Financial Aid

This program is available on-campus or online.

On Campus

Offered Admit Term(s) Application Deadline STEM Designated
Full-time Fall Rolling Yes
Part-time Fall Rolling No

Online

Offered Admit Term(s) Application Deadline STEM Designated
Part-time Fall Rolling No

Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Artificial Intelligence MS program, candidates must fulfill the following requirements:

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

TOEFL IELTS PTE Academic
88 6.5 60

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

Cost and Financial Aid

An RIT graduate degree is an investment with lifelong returns. Graduate tuition varies by degree, the number of credits taken per semester, and delivery method. View the general cost of attendance or estimate the cost of your graduate degree.

A combination of sources can help fund your graduate degree. Learn how to fund your degree

Additional Information

Prerequisites

Applicant must have college-level credit in Python programming and mathematics.

Online Degree Information

The online MS in artificial intelligence program offers core courses online in an asynchronous modality. Bridge courses, when assigned, are also taught asynchronously. Any live class or group meetings are usually optional in bridge and core courses. Elective courses for the online program are more limited than courses in the on-campus program and their availability will vary from semester to semester. Some electives will be synchronous only. Students in the online program have access to RIT computing and library resources. The online program is part-time, with students completing 1-2 courses per semester. Students will usually spend 10-12 hours per week per class, although this depends on course content and individual background knowledge. For details about the online learning experience, contact the program contact listed on this page. RIT does not offer international student visas for online study.

Online Tuition Eligibility
The online MS in artificial intelligence is a designated online degree program billed at a discount from the on-campus rate. Additional scholarships are not offered. View the current online tuition rate.

Online Study Restrictions for Some International Students

Certain countries are subject to comprehensive embargoes under US Export Controls, which prohibit virtually ALL exports, imports, and other transactions without a license or other US Government authorization. Learners from the Crimea region of the Ukraine, Cuba, Iran, North Korea, and Syria may not register for RIT online courses. Nor may individuals on the United States Treasury Department’s list of Specially Designated Nationals or the United States Commerce Department’s table of Deny Orders. By registering for RIT online courses, you represent and warrant that you are not located in, under the control of, or a national or resident of any such country or on any such list.

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Facilities

Resources

Current students in the artificial intelligence master’s program may refer to these resources for additional information.

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