Undergraduate Courses

This course serves as an introduction to the uses (and potential misuses) of data in a wide variety of social settings, including the exploration of contemporary techniques to analyze such data. Data acquisition, cleansing, management, analysis, and visualization will be addressed through hands-on projects. Project work will include contemporary social problems addressed using a dynamic set of resources and technologies. An emphasis will be placed on how insights gleaned from data analysis can be used to guide individual and group decision-making scenarios.

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.

Neuroscience has played a key role in the history of artificial intelligence (AI). The development of artificial neural networks was inspired by the knowledge gained from the study of brain functioning, with neuroscientists and psychologists, such as Donald Hebb, William McCulloch, and Geoff Hinton, contributing significantly to the establishment of the field. AI researchers aim to emulate human intelligence by building models and developing biologically-inspired architectures that can make decisions and solve problems in the same way that humans do. At the same time, artificial intelligence is increasingly used as a research tool in neuroscience to advance our understanding of how the human brain works and to accelerate neuroscience development. For example, by analyzing the massive amounts of experimental data on brain activity acquired using neuroimaging techniques, machine learning is used to uncover the patterns in brain activity and link them to specific cognitive and motor actions. This course reviews the fundamental ideas in computational neuroscience and connects the study of the brain to the concepts and research in artificial intelligence. The list of example topics includes neural coding, the biophysics of single neurons and neuron models, neural networks, biological and computational vision, adaptation and learning, machine learning, deep convolutional networks, memory, speech and language processing, and applications of computational neuroscience and artificial intelligence.

Neuroscience has played a key role in the history of artificial intelligence (AI). The development of artificial neural networks was inspired by the knowledge gained from the study of brain functioning, with neuroscientists and psychologists, such as Donald Hebb, William McCulloch, and Geoff Hinton, contributing significantly to the establishment of the field. AI researchers aim to emulate human intelligence by building models and developing biologically-inspired architectures that can make decisions and solve problems in the same way that humans do. At the same time, artificial intelligence is increasingly used as a research tool in neuroscience to advance our understanding of how the human brain works and to accelerate neuroscience development. For example, by analyzing the massive amounts of experimental data on brain activity acquired using neuroimaging techniques, machine learning is used to uncover the patterns in brain activity and link them to specific cognitive and motor actions. This course reviews the fundamental ideas in computational neuroscience and connects the study of the brain to the concepts and research in artificial intelligence. The list of example topics includes neural coding, the biophysics of single neurons and neuron models, neural networks, biological and computational vision, adaptation and learning, machine learning, deep convolutional networks, memory, speech and language processing, and applications of computational neuroscience and artificial intelligence.

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 are an integral part of the course.

An introduction to both foundational and modern machine learning theories and algorithms, and their application in classification and regression. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), Bayesian decision theory, parametric and non-parameteric classification models (e.g., SVMs and Nearest Neighbor models) and neural network models (e.g. Convolutional, Recurrent, and Deep Neural Networks). Programming assignments are required.

The course will introduce students to the application of intelligent methodologies in computer security and information assurance systems 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 its performance analysis. Students cannot take and receive credit for this course if they have credit for CSCI-735.

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 topics, and complete a group project in which they will design and execute experiments for search engine components.

This course examines current topics in Artificial Intelligence. This is intended to allow faculty to pilot potential new undergraduate offerings. Specific course details (such as prerequisites, course seminar, format, learning outcomes, assessment methods, and resource needs) will be determined by the faculty member(s) who propose a specific seminar 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. Students taking this course should have the 4th year status and completed MATH-190 Discrete Math, MATH-251 Probability and Statistics I, and MATH-241 Linear Algebra.

The course provides a basic introduction to the application of computation in the research and practice of the humanities, arts, and social sciences. The class offers students entry to work with archival theory and practice; textuality and electronic scholarly communication; data mining, analysis, and visualization; the spatial and temporal “turns;” game studies and digital arts. The course offers hands on experimentation with software platforms available to create scholarly and artistic production and theoretical approaches to digital presentation. Students will complete assignments requiring conceptual, aesthetic, and practical approaches to digital engagement with cultural materials. While no programming knowledge is required, students will design and create an online project using tools and platforms that are considered standard practice in the field, and reflect critically on the utility of digital techniques in their dialogue with the humanities.

The central focus of this course will be the excavation of textual, visual, and sonic materials, obsolete or emerging. The archaeological metaphor evokes both the desire to recover material traces of the past and the imperative to situate those traces in their social, cultural, and political contexts. How does the digital age imagine backwards to the Industrial Age and vice versa? Is it true that virtually everything that is being invented now for a digital age had its origins in the late nineteenth and early twentieth century industrial age? (inventions of telegraphy and telephony, electricity, photography, cinema, the automobile, the Dewey Decimal and Library of Congress classification systems, muckraking and sensationalist journalism, celebrity culture, the skyscraper, the office, the typewriter, the Brownie camera). We will take a research approach that explores moments in which both familiar and unfamiliar devices have yet to emerge as significant or disappear as curiosities.

The course will examine various contemporary and global issues of digital citizenship and new ethical challenges raised by digital technology. The course will raise questions regarding how digital technology has changed citizenship practices: Who has access to full citizenship, and why? What responsibilities are entailed in digital citizenship? Themes may include the nature and value of digital technology; the relations between digital technologies and knowledge-making/meaning-making; the value of information privacy; the role of digital media in society and human interactions; issues arising from the life-cycle of new digital tools and data repositories; and questions broadly related to questions of accessibility, representation, and sustainability as applied to digital technologies. Topics may also include research ethics, piracy and file sharing, hacktivism, copyright and fair use, end-user license agreements, alternative news media, and participatory culture. Students will take up both broad ethical issues and specific professional codes and policy in diverse domains.

The contemporary understanding of communication and narrative is quickly shifting in a world where media is ubiquitous. The "language of new media" is the thematic used in this course to discuss contemporary and historic forms of non-linear narrative. Students will explore the properties of non-linear, multi-linear, and interactive forms of narratives. This course will survey some of the possibilities, examining both traditional and new media such as oral storytelling, literature, poetry, visual arts, museum exhibits, architecture, hypertext fiction, Net Art, and computer games. Writers on communication culture, gaming, television, digital aesthetics, contemporary art and film, as well as synchronic narrative will be addressed. The focus is to develop critical tools to analyze contemporary media as well as a minimal level of practical implementation. Students will produce a final media project.

The objective of the course is to introduce students to computational modeling in economics. The course is intended for students who wish to learn what role computation can play in economics, how to create computational models for studying economic phenomena, and how to use these computational economic models to draw insights into economic phenomena. We will use programming languages such as Julia, Python and R for modeling and analysis.

The courses will introduce Artificial Intelligence and Machine Learning topics with practical examples of data, tools, and algorithms. In addition to C, C++, and Matlab, a scripting language (i.e. Python) will be used and taught throughout the course. The course will explore basic artificial intelligence techniques and their applications to engineering problems. Students will be introduced to the following AI foundations: probability and linear algebra, state spaces, algorithms, data processing, feature extraction, feature reduction, classification, and decision making. Some of the techniques and tools to be covered in this course are inference, regression, linear discriminant analysis, decision trees, neural networks, deep learning platforms and architectures, and reinforcement learning. Students are expected to have any of the following programming skills: C/C++, Matlab, Java, or any other high level programming language.

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.

The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.

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.

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 TCET-620.

Financial analytics is the use of business analytics methods and tools on financial data to solve problems such as investment and risk analysis, portfolio optimization, valuation, default modeling, and so on. This course introduces a contemporary tool (R or Python) and its use in solving these problems. In this hands-on course, students also learn about the field of fintech.

Events play an ever-growing role for individuals, organizations and communities or places/destinations at country, state, city levels. These entities stage a variety of events from birthdays, weddings, and festivals to conventions, trade expos and Olympics. This course examines unique design approaches and requirements of different personal, organizational and community events. Design knowledge and skills are a necessary to plan, execute and evaluate any type of event in an ever-changing industry. To respond to this complex demand, contemporary event planners must know how to interlink the process of purpose, people, and place (or venue) in diverse settings. This course incorporates venues and venue considerations into the design of events. Successful event production involves linking an event concept to design considerations such as entertainment, décor, audio systems, visualization, lighting systems, set design, tenting, and technical resources, are also addressed in this course. Beyond traditional event design and production, this course also focuses on broad skillsets such as experience personalization, social media platforms, chatbots, artificial intelligence, virtual reality, and augmented reality.

This class includes an overview of hotel management from its opening to continuing operations. It focuses on the integrated functions of the front office, housekeeping, engineering, security, food & beverage, human resources, and accounting, as well as considering their roles individually. Students will apply revenue management principles (e.g., capacity management, duration control, demand and revenue forecasting), costing (e.g., budgeting, marginal costing, standard costing and variance analysis, labor accounting, balanced scorecard) and interpret hospitality financial statements (uniform system of accounts for lodging and restaurants) to understand and manage organizational performance. The course addresses foundational metrics and definitions used by the hotel industry and provides an opportunity to complete a certification exam (CHIA: Certification in Hotel Industry Analytics) by STR through the American Hotel and Lodging Educational Institute.

This course explores introductory artificial intelligence concepts through both a theoretical and practical perspective, with an emphasis on how to apply these concepts in a game development context. In particular the course focuses on applying concepts such as search, reactive intelligence, knowledge representation, and machine learning to real-time situations and applications as relevant to the field of entertainment technology and simulation.

This course provides an introduction to the concepts and methods of image processing. The student will be exposed to sampling and quantization methods; descriptors and enhancement techniques based upon the image histogram; geometrical manipulations; interpolation and resampling; feature generation with direct application to image registration/stitching and redundancy reduction; pixel and object-level classification; frequency-domain applications, including automated image registration, data embedding, and image reconstruction; and image data redundancy and compression concepts. Emphasis is placed on efficient algorithmic implementations and applications, in an object-oriented development environment.

This course introduces the students to the governing equations for radiance reaching aerial or satellite based imaging systems. It then covers the temporal, geometric, spectral, and noise properties of these imaging systems with an emphasis on their use as quantitative scientific instruments. This is followed by a treatment of methods to invert the remotely sensed image data to measurements of the Earth’s surface (e.g. reflectance and temperature) through various means of inverting the governing radiometric equation. The emphasis is on practical implementation of multidimensional image analysis and examining the processes governing spatial, spectral and radiometric image fidelity.

This course builds on the principles of computing to introduce students to data analytics techniques commonly performed on digital data sets, using a variety of software tools. Students will learn what constitutes data and its associated social, ethical, and privacy concerns, common data acquisition and preparation techniques, and how to perform exploratory data analysis on real-world datasets from several domains. Common statistical and machine learning techniques, including regression, classification, clustering, and association rule mining will be covered. In addition, students will learn the importance of applying visualization for presenting and analyzing data. Students will be required to demonstrate oral and written communication skills through critical thinking homework assignments and both presenting and writing a detailed report for a project to analyze a data set of their choice. GCCIS majors may take this course only with the students’ home department approval, and may not apply these credits toward their degree requirements.

From artificial intelligence to gender and racial equity to international sanctions, the decisions we make and the actions we take are suffused with ethical dimensions. This seminar involves lively discussion and careful analysis of contemporary issues. Particular topics will change from one semester to the next, but each version of this course will apply ethical frameworks to conceiving, discussing, and striving toward the resolution of nuanced problems.

This course introduces main subfields of psycholinguistics, a study that deals with all aspects of human language performance: language acquisition, sentence processing/comprehension, and sentence production/speaking. Through readings on theoretical and experimental studies, findings and issues in first language acquisition, sentence processing, and sentence production are introduced. By discussing how speakers of different languages acquire, comprehend, and produce sentences, the course also examines interactions with language-specific, linguistic constraints and human language performances.

We will explore the relationship between language and technology from the invention of writing systems to current natural language and speech technologies. Topics include script decipherment, machine translation, automatic speech recognition and generation, dialog systems, computational natural language understanding and inference, as well as language technologies that support users with language disabilities. We will also trace how science and technology are shaping language, discuss relevant artificial intelligence concepts, and examine the ethical implications of advances in language processing by computers. Students will have the opportunity to experience text analysis with relevant tools. This is an interdisciplinary course and technical background is not required.

Classical game theory models conflict and cooperation between rational decision-making agents with hidden parameters. Topics include matrix games, Nash equilibria, the minimax theorem, prisoner’s dilemma, and cooperative games. Applications can include adaptive or statistical decision theory, artificial intelligence (online learning, multi-agent systems), biology (evolutionary games, signaling behavior, fighting behavior), economics and business (auctions, bankruptcy, bargaining, pricing, two-sided markets), philosophy (ethics, morality, social norms), and political science (apportionment, elections, military strategy, stability of government, voting).

This course examines modeling, instrumentation, and measurement of electrical, mechanical, fluid, and thermal systems containing elements such as sensors and actuators used in feedback control systems. Analytical and experimental techniques of general importance in systems engineering are presented, including sensor utilization in feedback control. Engineering measurement fundamentals, including digital and frequency domain techniques noise and error analysis are covered. Closed-loop system analysis will include the use of proportional, integral, and derivative elements to control system response. Hands-on projects and laboratories are utilized to reinforce fundamental measurement and control system concepts. Software skills include the use of MATLAB and the graphical programming language, LABVIEW.

To be successful in our globally-networked business environment, contemporary management professionals must have a strong grounding in the principles of information and information technology. This course provides an introduction to the field of management information systems (MIS), including the tools and techniques for managing information and information technologies within organizations. We place a particular emphasis on the nature of systems, the role of information in business processes, the management of data, and the planning of MIS design projects.

The course is intended to provide an integrative foundation in the field of business intelligence at both the operational and strategic levels. Students will experience a variety of contemporary tools to analyze complex business data and arrive at a rational solution. Topic such as data warehousing, visualization and data mining will be covered, along with other topics relevant to the field of business intelligence. The computer will be used extensively throughout the course.

Advanced study of MIS topics reflecting contemporary issues and/or current technological advancements impacting the development, implementation and management of information systems in organizations. Seminar topics have ranged from new technological developments to management security issues in MIS systems. Topics for a specific semester will be announced prior to the course offering.

Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI). Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize wasted online and offline marketing dollars. It also provides marketers with the information necessary to help support company investment in marketing strategy and tactics. This course provides the participant with the necessary knowledge and practical insights that will help a marketing manager get more out of available data and take strategic advantage of the analysis. This interactive, participatory course is designed to answer key questions: “What is marketing analytics, how can marketing analytics improve my marketing efforts and how can I integrate marketing analytics into my business?

An examination of search engine marketing strategies to maximize site traffic, lower customer acquisition costs and boost conversion rates. Marketing frameworks provide the basis for the hands-on examination of search engine marketing and web analytics.

The interrelationship between packaging and marketing, detailing how the retail consumer package can be used as a marketing tool. Concentrates on a systematic approach to developing an optimum package for a given product to meet the demands of the retail market and end user. Students gain practice in the development of a complete package system.

An introduction to some of the philosophical dimensions of the search for world peace, including the elements that would constitute a just and lasting peace, nations as moral entities, justice and national self-interest, force and violence, the morality of the use of force, peace-making and peace-keeping groups.

Technology is a ubiquitous and defining force in our world. This course investigates how our conceptions of technology have emerged within philosophy, as well as the role technology plays in shaping how we live and how we reflect upon questions of meaning and value in life. Technological modes of understanding, organizing and transforming the world shape our relationships with others, with ourselves and with nature at fundamental levels. We will explore how these modes have emerged and why they emerged so predominantly within a Western social and intellectual context.

This course examines the political promises and challenges of artificial intelligence (AI) through the consideration of the technological trajectories and possible scenarios of advanced AI. Possible discussion topics may include: The compatibility of AI with the political principles of liberty, equality, and the pursuit of happiness to understanding what an AI arms race between countries might entail. Domestically, will the prospect of greater job automation produce mass unemployment with severe consequences? Globally, will the weaponization of AI make going to war easier? Questions like these are inherently political and the movement toward greater AI capabilities raises the more general question of whether humanity will be able to regulate, both domestically and globally, a technology that promises to surpass all technology that has gone before it. This course will seek to anticipate and prepare for the risks that advanced AI poses to domestic and global politics. The goal will be to think about how advanced AI can be prudentially oriented toward beneficial practices for the sake of the political good.

This course examines how advances in computer science, robotics, biotechnology and other emerging technologies are being applied to organized violence. Emphasized are the ways that lethal uses of unmanned aerial vehicles (drones), warbots with artificial intelligence, cyber-attacks, and other emerging technologies are changing or will change the character of war and the societies that enact it. Special attention is given to the ethical and legal dilemmas these technologies present to citizens, states, and the international community, assessing both the harm and the good that they make possible.

Introduction to the field of psychology. Provides a survey of basic concepts, theories, and research methods. Topics include: thinking critically with psychological science; neuroscience and behavior; sensation and perception; learning; memory; thinking, language, and intelligence; motivation and emotion; personality; psychological disorders and therapy; and social psychology.

This course examines how people perceive, learn, represent, remember and use information. Contemporary theory and research are surveyed in such areas as attention, pattern and object recognition, memory, knowledge representation, language acquisition and use, reasoning, decision making, problem solving, creativity, and intelligence. Applications in artificial intelligence and human/technology interaction may also be considered.

Industrial and organizational (I/O) psychology is a branch of applied psychology that is concerned with efficient management of an industrial labor force and especially with problems encountered by workers in a mechanized environment. Specific areas include job analysis, defining and measuring job performance, performance appraisal, tests, employment interviews, employee selection and training, and human factors. This course covers the basic principles of the above areas as well as applications of current research in I/O psychology.

This course is intended for students in the cognitive track. This course examines the structure of human language and its relationship to thought, and surveys contemporary theory and research on the comprehension and production of spoken and written language. In addition, we will discuss categorization, representation of knowledge, expertise, consciousness, intelligence, and artificial intelligence. Topics on language and thought in non-human animals may also be covered. Part of the cognitive track for the psychology degree program.

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.

This course focuses on the technology and application of robots and automation in the modern manufacturing environment. It will provide a thorough understanding of robotic hardware and software. The hardware aspects include robot configurations, drive mechanisms, power systems (hydraulic, pneumatic, and servo actuators), end-effectors and end-of-arm-tooling, sensors, control systems, machine vision, programming, safety, and integration. The software aspect deals with the various methods of textual and lead through programming commonly found on commercial robotic systems, as well as simulation systems offered by robot manufacturers. Digital Interfacing of robots with other automation components such as programmable logic controllers, computer-controlled machines, conveyors, is introduced. Robotic cell design and the socio-economic impact of robotics are also discussed. This course also has a strong experiential component that emphasizes hands-on training. This course may be cross-listed with RMET-685. Students may not take and receive credit for this course if they have already taken RMET-685. College-level programming experience in at least one computer language strongly recommended.

The use of statistical models in computer algorithms allows users to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Data mining and Machine learning are interdisciplinary fields at the intersection of statistics, computer science, applied mathematics which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision. The course will provide an introduction to Statistical Machine Learning and its core models and algorithms.

Science Technology and Values explores the concepts and effects of science and technology on society, and analyzes the relationship between science and technology, asking questions such as: How each has come to play a major role today, and how have science and technology affected and been affected by human values, despite longstanding assumptions that science and technology are value-free? Environmental aspects of science and technology will also be examined from interdisciplinary perspectives. Key themes include the practical and theoretical relationships between science, technology, and power.

STP eExamines 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.

Technology has an impact on every aspect of our social lives. With each advance, unanticipated problems emerge, leading to complex debates about addressing the negative consequences. This course highlights the social, ethical, and humanistic challenges of assorted technologies, past and present. We will investigate how various technologies developed and compare the expected effects of the new technologies with the actual results.

How does artificial intelligence impact society? In this course, we will examine how AI and related algorithmic technologies shape, and are shaped by, societal issues and factors. We will critically examine historical and contemporary research and applications of AI from social, cultural, and policy perspectives. Students will encounter a variety of perspectives from science and technology studies, the humanities, and the social sciences and use real-world cases in order to analyze how AI technologies may differentially impact people, communities, and societies.

Disasters represent a disruption to daily life, with technological disasters defined as disasters resulting from human-made causes, where failures in modern technology create both acute and ongoing dangers for communities. This course focuses on how human technological advances can have adverse impacts on the communities those innovations are meant to improve. Through an investigation of technological systems and case-specific technologies, combined with ecological, social, and political systems, the causes, consequences, and long-term implications of technological disasters are considered. The course will examine cases that range from the actual to the anticipated, such as the New Orleans levee failures, Flint water crisis, Dalkon shield contraception, large-scale networked hacks, CRISPR-created and/or naturally-occurring superviruses, voting poll technology failures, and AI, in the context of the societal systems of modern industrial capitalism. Special attention will be paid to aspects of social vulnerability which make the impacts of technological disasters different for various sub-populations within their respective communities.

We are not alone. With Artificial Intelligence, Smart Technologies, and advances in medical, workplace, and in-home robotics, humans have entered an era in which social relationships with robots is an everyday occurrence. Robots as pets, caregivers, and friends are marketed to old and young alike with the anticipation that some form of relationship will be built between person and robot. But what does it mean to have a robotic companion? Can they be programmed to care for us, and even love us? Are our social connections with these robots “real” or “authentic” or are they misplaced hopes of connection? What can, and should, they do? Ethical questions emerge when exploring the uses of robot assistants with vulnerable populations in medical settings including care of elderly dementia patients and neurodivergent minors. Social considerations of trust and misuse of data are also hotly debated. What should the robot do and what should it not do? This is the world of social robotics. For robots to live among “us” and help define “us” how should they act and how should we react in return? This course examines Social Robotics by offering a survey of topics necessary to better understand the world of human-robot relations and ponders what futures we are building with robot companions. Topics to be discussed may include robot rights, Lovotics, authenticity, electronic personalities, and the Uncanny Valley. This course fulfills Ethical Perspectives and Social Perspectives.

Yes, you are being watched. In this course, we consider how surveillance technologies permeate all areas of life for humans, animals, and robots. From smart houses that are always listening, to tracking devices for wildlife research, or networked AI-enhanced robots, the role of surveillance is an under-examined constant in post-millennium life. Whether surveilled by government agencies for social control, private corporations for profit, family members for safety, or friends and the public for amusement, the power dynamics of how surveillance data are gathered, stored, managed, and distributed reveal new social and ethical relationships, while also reinforcing pre-existing patterns of bias and inequality. The ethical impacts of surveillance technologies press the limits of civil society, privacy assumptions, and even animal rights, when gathering and storing data without consent or among vulnerable populations. In this course, you will discover the promises and perils of surveillance technology by applying insights from STS (science and technology studies) and other interdisciplinary fields.

In this course, students will examine the ways in which “nature,” broadly conceived, has been quantified, standardized, and in many cases commodified in the modern West – often in the context of the natural sciences, government bureaucracies, capitalist markets, or some combination of the three. Reading and discussing broadly across history, science studies, anthropology, philosophy, and ecology, students will gain multidisciplinary perspectives on modern informational thinking, and develop analytical tools for assessing contemporary issues related to the quantified environment.

The developing cybernetic organism or cyborg challenges traditional concepts of what it means to be human. Today medical science and science fiction appear to merge in ways unimagined a century ago. By exploring scientific and cultural theories, science fiction, and public experience, this class examines the history and potential of the cyborg in Western cultures.

Popular attention often focuses on a few prominent women in computing history, such as Ada Lovelace, Grace Hopper, and the ENIAC programmers. But many more women were part of this history: as inventors, programmers, operators, and users of information and communication technologies. Investigating their legacies, we will discuss in this course how computing turned into an increasingly masculine field, what it meant for women and men to work in a male-dominated field, how the gendering of computing technologies and algorithms affected the identities and lives of their users, and how gender intersected online and offline with other dimensions of diversity, such as class, race, and ability. This course provides the theoretical concepts and historical overview that allow for a historically informed discussion of women, gender, sexuality, and computing today.