Graduate Courses

The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction.

This is an advanced course in statistical inference and data analysis for the astrophysical sciences. Topics include Bayesian and frequentist methods of parameter estimation, model selection and evaluation using astrophysical data. Specific applications, such parameter estimation from gravitational wave signals, or analysis of large data sets from imaging, spectroscopic or time domain surveys will be discussed. Computational methods including Markov Chain Monte Carlo, with other topics such as machine learning, and time series analysis included at the discretion of the instructor.

This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.

This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.

Students apply their mathematical, data analytic, and integrative business analytics skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.

The identification of genetic causes of disease has been one of the major modern scientific breakthroughs. This course examines a range of inherited diseases, how causative genetic variations were or are being identified, and what this means for the treatment of the diseases. Scientific literature will be utilized, both current and historical.

Machine learning is a fast-developing field of artificial intelligence (AI) with many applications in life sciences. The huge amount of genomic data can be analyzed and interpreted by machine learning techniques. This course introduces basic concepts of machine learning models and demonstrates how these models can solve complex problems in life sciences. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the machine learning toolkits through a tutorial. Main topics cover three branches of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Instead of applying different machine learning methods to different datasets, the course aims to apply different methods to the same datasets so that students are able to compare the performance and pros/cons of the methods. Hands-on exercises will be provided in both lectures and weekly labs. A group project will be given at the end of the semester so that students can apply machine learning methods to the datasets they are interested in. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique to apply for a particular dataset and need, engineer features to meet that need, and write code to carry out an analysis.

This course will introduce traditional multivariate statistical methods and multi-model inference, as well as iterative computational algorithms (i.e. Bayesian methods and machine learning) appropriate for graduate students conducting or planning to conduct a graduate research project. The course will focus on the proper application of methods to a sample data sets using statistical programming software and graphics and will forego the more in-depth analytical mathematical exposition that you might see in a math course, so that we can cover a larger variety of methods and spend more time implementing them in code. Practical examples will often derive from the fields of biology, environmental science, or medicine, however the statistical methods we cover will also have much broader application within modern data science. The ultimate goal will be to learn when and where to correctly apply a given method to real questions about real data. Class time will be devoted to introductory lecture, programming language demonstrations with a common dataset, and open discussions of potential applications, including in-class studio hours to help with homework. Students should be prepared to learn to write code scripts that will manipulate statistical tests and graphical output. However, no background experience with programming is assumed. All software used in the course is open-source and students will be required to set up and run weekly assignments on their own laptop computer or on a computer borrowed from the library or RIT’s computer lab.

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.

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.

Deep learning is an area of machine learning that has enabled enormous progress on long-standing problems in visual analytics and machine perception. This course will start with a graduate-level introduction to deep learning, and review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. After gaining the prerequisite background knowledge, the class will review the latest deep learning algorithms for computer vision and machine perception. Students will read and present recent papers on visual analytics topics, including eye tracking, image classification, video understanding, model explanation, etc. The course will make an emphasis on approaches with practical relevance, and prepare students to use state-of-the-art deep learning algorithms for processing and understanding highly structured data such as images, videos, and time-series. Students are expected to have programming experience and to be comfortable with probability, linear algebra and calculus. No prior background in machine learning or pattern recognition is required.

This course covers an overview of robotics topics with an AI influence. Includes hands-on laboratory with low level microcontroller programming driving a Lynxmotion 4WD chassis. Course has a strong emphasis on robotics related input and output device inter-facing. Course topics include microcontrollers, control systems, vision, path planning localization, and machine learning. Term project of student choosing emphasizes a specific robotic topic.

Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and 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.

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.

This course studies multiple aspects of cognitive radios and their operation in a cognitive network. Cognitive radios are an artificial intelligence agent that, instead of operating in the more common three-dimensional physical space that surround us, it learns and operates in the “virtual” space of the radio spectrum. Topics to be covered include an overview of wireless channels and wireless communications, cognitive radios network paradigms, spectrum sensing and dynamic spectrum access, spectrum exploration and exploitation through game theory and machine learning, cross-layer cognitive radios and cognitive networking.

This course is a semester-long project-based course, where students learn to select and apply machine learning and data science (ML/DS) techniques to solve cybersecurity problems. Through learning-by-doing, students will discover cybersecurity challenges and how ML/DS can help overcome the challenges as well as the limitations of ML/DS. Students will explore and choose appropriate ML/DS approaches, design and conduct experiments with open-domain cybersecurity data, and deduce and present findings to practice analytical and critical thinking skills. The course will progress in tightly guided and coupled stages: data and feature analysis, literature review and problem discovery, ML technique exploration, experimental design, result interpretation and analysis, professional project dissemination, and constructive peer reviews.

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 course focuses on social media research and ethics of applying various methodological approaches to study public data, users and messages. Students will be introduced to a variety of techniques and concepts used to obtain, monitor and evaluate social media content with a focus on how the analytics could inform communication strategies. During the course, students will also learn how to design and evaluate social media-based research studies.

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.

An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required.

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.

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.

This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. Bayesian networks, Markov models), and reinforcement learning. Programming assignments are required.

An introduction to the theories and techniques used to construct search engines. Topics include search interfaces, traditional retrieval models (e.g., TF-IDF, BM25), modern retrieval techniques (e.g., neural reranking and retrieval), search engine evaluation, and search applications (e.g., conversational IR, enterprise search). Students will also review current IR research, provide written summaries of current research papers, and complete a group project in which they will design and execute experiments for search engine components.

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.

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.

The course will introduce students to the application of intelligent methodologies applications in computer security and information assurance system design. It will review different application areas such as intrusion detection and monitoring systems, access control and biological authentication, firewall structure and design. The students will be required to implement a course project on design of a particular security tool with an application of an artificial intelligence methodology and to undertake research and analysis of artificial intelligence applications in computer security.

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.

This course examines current topics in Artificial Intelligence. This is intended to allow faculty to pilot potential new graduate offerings. Specific course details (such as prerequisites, course topics, format, learning outcomes, assessment methods, and resource needs) will be determined by the faculty member(s) who propose a specific topics course in this area. Specific course instances will be identified as belonging to the Artificial Intelligence cluster, the Computer Graphics and Visualization cluster, the Security cluster, or some combination of these three clusters.

The course provides students an opportunity to explore methods and applications in cyber analytics with advanced machine learning algorithms including deep learning. Students will learn how to use machine learning methods to solve cybersecurity problems such as network security, anomaly detection, malware analysis, etc. Students will also learn basic concepts and algorithms in machine learning such as clustering, neural networks, adversarial machine learning, etc. A key component of the course will be an independent exploratory project to solve a security program with machine learning algorithms. Students taking this course should have knowledge in Discrete Math, Probability and Statistics, and Linear Algebra. Students should also be able to program in Python.

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.

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 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.

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.

The course will start with the history of artificial intelligence (AI) and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many AI techniques have been developed and applied to solve real life problems. This course will explore a variety of AI techniques and their applications and limitations. Some of the AI topics to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, machine learning, reinforcement learning, and real-world applications of AI. Students are expected to have solid programming skills, understanding of probability and linear algebra, and statistics. Students will write a conference-style paper based on a research project.

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.

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.

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.

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.

This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

This Ph.D. research seminar focuses on the two roles of technology in accounting and finance research in particular, and business research generally. First, the world of technology which includes information technology and analytics, has influenced research methods with techniques such as sentiment analysis and machine learning. Second, technology has transformed the practice of accounting and finance, through innovations such as the blockchain and has led to distinct areas of research such as fintech. This seminar will cover both aspects and has the objective of (a) allowing access to cutting edge research techniques and (b) developing research questions in tech related areas.

This course introduces students to the application of data analytical methods and software in the analysis and management of hospitality operations. The hospitality industry is among the most data-intensive industries and this course prepares students to identify, collect, and analyze data in support of organizational decision-making. Students will learn the application of data analytics in improving industry revenues, optimizing and managing marketing programs, and in human capital management.

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.

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.

Students perform paid professional work related to Artificial Intelligence while they are considered enrolled full-time. When registered for co-op, students complete a work report. To receive a satisfactory grade, the student co-op work report should be largely consistent with employer evaluation. The minimum is 35 hours of co-op work per week for the semester's duration (summer, fall, or spring). Co-op is an enrichment opportunity and optional for degree completion. To be eligible, a student must have completed assigned bridge coursework and between 15 credits to two semesters of MS on-campus coursework, be a full-time student in good standing (cumulative GPA of 3.0 or better or a semester GPA of 3.0 or better in the semester immediately preceding the requested co-op term), and attend a co-op orientation at RIT.

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.

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.

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.

This course explores artificial intelligence concepts and research through both a theoretical perspective and a practical application to game development. In particular the course focuses on AI concepts and paradigms such as search and representation, reasoning under uncertainty, intelligent agents, biologically inspired computing and machine learning to real-time situations and applications as relevant to the field of entertainment technology and simulation.

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.

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.

This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required.

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 course is designed to help students, regardless their backgrounds, to identify global business opportunities, possess necessary analytical skills to evaluate these opportunities, and understand the strategies to explore these opportunities to serve transnational businesses’ goals. Students will be exposed to a variety of analytical skill sets such as collecting and analyzing institutional and primary international business data, reading the multinational firm-level data and understanding how global expansion impacts firms’ bottom lines, developing foreign exchange hedging strategies, and apprehending the basic practices of international trade and foreign investment.

This course provides a functional introduction to programming, data structures, elemental computational theory, and data exploration for graduate students from non-computing backgrounds. Students prepare for working with data and artificial intelligence techniques.

This course provides students with exposure to foundational data analytics technologies, focusing on unstructured data. Topics include unstructured data modeling, indexing, retrieval, text classification, text clustering, and information visualization.

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.

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.

In systems where parameters can vary, we often want to understand the effects that some variables exert on others and their impact on system performance. “Data Analytics and Predictive Modeling” describes a variety of machine learning and data analysis techniques that can be used to describe the interrelationships among such variables. In this course, we will examine these techniques in detail, including data cleansing processes, data clustering, associate analysis, linear regression analysis, classification methods, naïve Bayes, neural networks, random forests, variable screening methods, and variable transformations. Cases illustrating the use of these techniques in engineering applications will be developed and analyzed throughout the course. Note: Students required to take ISEE-561 for credit may not take ISEE-661 for credit.

Computational techniques for solving constrained optimization problems. Linear programming, the Simplex method and variations, duality and sensitivity testing.

An introduction to the mathematical foundations of integer programming and nonlinear optimization techniques. Study of algorithms and computer-aided solutions for applied optimization problems.

Simulation Analysis focuses on simulation design, analysis, and applied research methods for industrial and service systems. In particular, the course covers discrete-event, agent-based, and continuous simulation modeling approaches; data driven simulation models; design and analysis of simulation experiments and optimization; artificial intelligence (AI) simulation methods; and Industry 4.0/Digital Twin simulation.

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.

This course presents the primary concepts of decision analysis. Topics important to the practical assessment of probability and preference information needed to implement decision analysis are considered. Decision models represented by a sequence of interrelated decisions, stochastic processes, and multiple criteria are also addressed. We cover EMV and Non-EMV decision-making concepts. Finally, the organizational use of decision analysis and its application in real-world case studies is presented.

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.

This course serves as a bridge course that builds the mathematical foundations needed for the IDAI-620 course, Mathematical Methods for Artificial Intelligence, a course introducing the mathematical background for AI systems in the MS in AI program. It focuses on the basic constructions, structures, and results in four key areas: (1) linear algebra (vectors, matrices, and their operations) (2) optimization theory (multivariable functions and their calculus) (3) probability and statistics (basic combinatorics, elementary statistics) and (4) numerical analysis (basic notions of approximation).

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.

This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.

This Ph.D. research methodology course will introduce students to contemporary and advanced analytics techniques related to data acquisition, data preparation, data mining, and data reporting. Students will engage in hands-on experience with different techniques and will demonstrate the ability to carry a research project on their own using a combination of techniques taught in class.

This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

This course is an introductory graduate-level overview of techniques in and applications of data analysis in physics and related fields. Topics examined include noise and probability, model fitting and hypothesis testing, signal processing, Fourier methods, and advanced computation and simulation techniques. Applications are drawn from across the contemporary physical sciences, including soft matter, solid state, biophysics, and materials science. The subjects covered also have applications for students of astronomy, signal processing, scientific computation, and others.

This is a graduate-level course on a topic that is not part of the formal graduate physics curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures.

Leveraging big data to deliver solutions to complex challenges requires an organizational leadership that is responsible for understanding and directing these approaches to achieve their business goals. Rather, organizational leadership is responsible for understanding and directing these approaches to achieve business goals. Toward this end, this course provides students with the knowledge and confidence needed to imbue organizations with innovative, efficient, and sustainable aspects that will carry them into the future through an understanding and application of business analytics and artificial intelligence (AI). Students will gain a theoretical and working knowledge of data science, enabling the identification of the challenges that analytics, machine learning, and artificial intelligence can address. An introduction to the ethical and social implications of analytics and AI in terms of guiding an organization’s strategic assets for the future will also be presented.

This course introduces students to foundational skills in data analytics, with a focus on mathematical foundations. Students will explore topics that form the backbone of modern data analytics such as machine learning, data mining, artificial intelligence and visualization. Tools for statistics will be introduced to students for how to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making.

This course explores the emerging technologies that are driving the acceleration of applications and the data produced by them Big Data and its 5V characteristics – volume, velocity, veracity, variety and value –across industry, research and academia. Students will be introduced to a range of complemented technology disciplines like cybersecurity, virtual content delivery, artificial intelligence, and smart cities where the uses of real-time analysis on big datasets are applied. Particular focus will be paid to a review of a number of industry verticals and data related to how emerging technologies are used with an emphasis on privacy and ethical considerations.

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.

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.

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.

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.

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.

This course deals with the higher level of topics relating to automation control systems engineering. Learning different programming languages, troubleshooting techniques, advanced programming instructions, the use and application of Human Machine Interface (HMI) panels, analog devices uses and applications, advanced system design, networking and an introduction to Industry 4.0 are all covered in this course. Students will be expected to develop the main system and all subsystems required to solve an automation problem. Students with no/limited PLC programming and automation system design knowledge are required to take RMET-340/341 as a bridge course. Students may take and receive credit for RMET-571 or RMET-671, not for both.

Technology and application of robots and CNC in an integrated manufacturing environment is the focus of this course. An introductory understanding of robotic hardware and software will be provided. The hardware portion of this course involves robot configurations, drive mechanisms, power systems (hydraulic, pneumatic and servo actuators), end-effectors, sensors and control systems. The software portion of this course involves the various methods of textual and lead through programming. Digital interfacing of robots with components such as programmable logic controllers, computer-controlled machines, conveyors, and numerical control will be introduced. Robotic cell design and the socio-economic impact of robotics will also be discussed. This course also has a strong laboratory component that emphasizes hands-on training. This course may be cross listed with RMET-585. Students may not take and receive credit for this course if they have already taken RMET-585.

Analytics in service organizations is based on four phases: analysis and determination of what data to collect, gathering the data, analyzing it, and communicating the findings to others. In this course, students will learn the fundamentals of analytics to develop a measurement strategy for a given area of research and analysis. While this measurement process is used to ensure that operations function well and customer needs are met; the real power of measurement lies in using analytics predicatively to drive growth and service, to transform the organization and the value delivered to customers. Topics include big data, the role of measurement in growth and innovation, methodologies to measure quality, and other intangibles.

This course is designed to provide the student with solid practical skills in implementing basic statistical and machine learning techniques for the purpose of predictive analytics. Throughout the course, many real world case studies are used to motivate and explain the strengths and appropriateness of each method of interest. In those case studies, students will learn how to apply data cleaning, visualization, and other exploratory data analysis tools to a variety of real world complex data. Students will gain experience with reproducibility and documentation of computational projects and with developing basic data products for predictive analytics. The following techniques will be implemented and then tested with cross-validation: regularization in linear models, regression and smoothing splines, k-nearest neighbor, and tree-based methods, including random forest.

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.

STP examines how local, state, federal and international policies are developed to influence innovation, the transfer of technology and industrial productivity in the United States and other selected nations. It provides a framework for considering the mechanisms of policy as a form of promotion and control for science and technology, even once those innovations are democratized and effectively uncontrollable. Further focus is dedicated to the structure of governance inherent in U.S. domestic policy, limits of that approach, the influences of international actors, and utilizing case studies to demonstrate the challenges inherent in managing differing types of technology. This seminar is restricted to degree-seeking graduate students or those with permission from the instructor.

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.

Machine learning has applications in a wide variety of fields ranging from medicine and finance to telecommunications and autonomous self-driving vehicles. This course introduces machine learning and gives you the knowledge to understand and apply machine learning to solve problems in a variety of application areas. The course covers neural net structures, deep learning, support vector machines, training and testing methods, clustering, classification, and prediction with applications across a variety of fields. The focus will be on developing a foundation from which a variety of machine learning methods can be applied. Students may not take and receive credit for this course if they have already taken EEET-520.