Artificial Intelligence Master of Science Degree

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

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 SciencesCollege of Liberal ArtsCollege 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.
 

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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

Salary and Career Information for Artificial Intelligence MS

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.

Curriculum for Artificial Intelligence MS

Artificial Intelligence, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
IDAI-610
Fundamentals of Artificial Intelligence
3
IDAI-620
Mathematical Methods for Artificial Intelligence
3
IDAI-700
Ethics of Artificial Intelligence
3
IDAI-710
Fundamentals of Machine Learning
3
IDAI-720
Research Methods for Artificial Intelligence
3
 
Program Elective
3
Second Year
Choose one of the following tracks:
6
   IDAI-780
Capstone Project, plus one additional Program Elective
 
   IDAI-790
Research & Thesis
 
 
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).
CSCI-737
Pattern Recognition
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lecture 3 (Fall).
CSEC-720
Deep Learning Security
This course covers the intersection of cybersecurity and deep learning technologies such as CNNs, LSTMs, and GANs. Topics include the application of deep learning to traffic analysis, Deepfake detection, malware classification, fooling deep learning classifiers with adversarial examples, network attack prediction and modeling, poisoning attacks, and privacy attacks like model inversion and membership inference. 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 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-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 equivalent course.) Lecture 3 .
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
ENGL-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. The course will have relevance to various disciplines in the humanities, sciences, computational, and technical fields. We will discuss problems that involve different components of the language system (such as meaning in context and linguistic structures). Students will additionally work on modeling and implementing natural language processing and digital text solutions. Students will program in Python and use a variety of relevant tools. Expected: Programming skills, demonstrated via course work or instruction approval. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).
ENGL-682
Natural Language Processing II
Study of a focus area of increased complexity in computational linguistics. 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: ENGL-681 or equivalent course.) Lecture 3 (Spring).
ENGL-684
Speech Processing II
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. 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
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).
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 (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 (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 (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 (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).
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).

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-737
Pattern Recognition
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lecture 3 (Fall).
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-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: 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).

Admission Requirements

To be considered for admission to the MS in artificial intelligence, candidates must fulfill the following requirements:

  • Complete an online graduate application. Refer to Graduate Admission Deadlines and Requirements for information on application deadlines, entry terms, and more.
  • Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
  • Hold a baccalaureate degree (or US equivalent) from an accredited university or college.
  • Recommended minimum cumulative GPA of 3.0 (or equivalent).
  • Submit a current resume or curriculum vitae.
  • Two letters of recommendation are required. Refer to Application Instructions and Requirements for additional information.
  • Not all programs require the submission of scores from entrance exams (GMAT or GRE). Please refer to the Graduate Admission Deadlines and Requirements page for more information.
  • Submit a personal statement of educational objectives. Refer to Application Instructions and Requirements for additional information.
  • Have college level credit or practical experience in computer programming and mathematics.
  • International applicants whose native language is not English must submit official test scores from the TOEFL, IELTS, or PTE. Students below the minimum requirement may be considered for conditional admission. Refer to Graduate Admission Deadlines and Requirements for additional information on English language requirements. International applicants may be considered for an English test requirement waiver. Refer to the English Language Test Scores section within Graduate Application Materials to review waiver eligibility.

 

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Resources

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

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