Information Course Schedule Spring 2024

Surveying history through the lens of information and information through the lens of history, this course looks across time to consider what might distinguish ours as “the information age” and what that description implies about the role of “information technology” across time. We will select moments in societies’ development of information production, circulation, consumption, and storage from the earliest writing and numbering systems to the world of Social Media. In every instance, we’ll be concerned with what and when, but also with how and why. Throughout we will keep returning to questions about how information-technological developments affect society and vice versa?

Three hours of lecture per week. Methods and concepts of creating design requirements and evaluating prototypes and existing systems. Emphasis on computer-based systems, including mobile system and ubiquitous computing, but may be suitable for students interested in other domains of design for end-users. Includes quantitative and qualitative methods as applied to design, usually for short-term term studies intended to provide guidance for designers. Students will receive no credit for 114 after taking 214.

This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, FastAPI, Docker, RDBMS/NoSQL databases, and Celery/Redis, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using microservices that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

Our world is rife with misinformation. This is a course about misinformation and “calling bullshit” — spotting, dissecting, and publicly refuting — false claims, but also inferences based on quantitative, statistical, and computational analysis of data. Spotting misinformation; causal fallacies; statistical traps; data visualization; big data; interpreting scientific claims; fake news and social media; refutation techniques. Prior math or statistics background unnecessary.

Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make.

This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability.

This course uses examples from various commercial domains — retail, health, credit, entertainment, social media, and biosensing/quantified self — to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work.

This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey, focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings.

This course is a graduate-level introduction to HCI research. Students will learn to conduct original HCI research by reading and discussing research papers while collaborating on a semester-long research project. Each week the class will focus on a theme of HCI research and review foundational and cutting-edge research relevant to that theme. The class will focus on the following areas of HCI research: ubiquitous computing, social computing, critical theory, and human-AI interaction. In addition to these research topics the class will introduce common qualitative and quantitative methodologies in HCI research.

Three hours of lecture per week. Prerequisites: Graduate standing. As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"?

Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers, wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory.

Three hours of lecture per week. This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure.

This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology has facilitated the development of better data gathering.

Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data.

This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products.

This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization.

This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend.

This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques, and discussion, in addition to lectures and readings. Two hours of lecture per week.

Three hours of seminar per week.  This seminar reviews current literature and debates regarding Information and Communication Technologies and Development (ICTD). This is an interdisciplinary and practice-oriented field that draws on insights from economics, sociology, engineering, computer science, management, public health, etc.

As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Through a careful reading of recent research and through hands-on analysis of large-scale datasets, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, machine learning, information science, and computational social science. Students will also conduct original statistical and computational analysis of real-world data.

This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic, students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization.

Our world is rife with misinformation. This is a course about misinformation and “calling bullshit” — spotting, dissecting, and publicly refuting — false claims, but also inferences based on quantitative, statistical, and computational analysis of data. Spotting misinformation; causal fallacies; statistical traps; data visualization; big data; interpreting scientific claims; fake news and social media; refutation techniques. Prior math or statistics background unnecessary.

For this course, we are going to tackle one of the world’s biggest challenges (voted on by the students). We will organize as an innovation lab tasked with developing new products and so as to better understand the principles, process, and outputs of interaction design. The goal will be to be able to apply the concepts and frameworks we cover in class to a real problem space.

Students will be responsible for developing a robust prototype over the final few weeks of the course. They will also write a reflection on the prototype development process, drawing on the theoretical concepts covered in the course. On the last day of class, students will present their work to a panel of industry experts for feedback.

The Future of Cybersecurity Working Group (FCWG) assembles students, researchers, and faculty from across the campus with a shared interest in security. We read and discuss the current cybersecurity scholarship and workshop projects related to cybersecurity. Our goal is to support critical inquiry into security and explore how it relates to political science, law, economics, the military, and intelligence gathering. Students are required to participate in weekly sessions, present short papers on the readings, and write response pieces.

This seminar explores markets as a social and information technology, examining how they are constructed and how they function in the digital economy. The course connects ongoing developments in markets and market design to longstanding lines of scholarship, engaging with both foundational and contemporary readings across fields such as information economics, social theory, science and technology studies, and information science.

Topics include examining the role of markets as information-processing technologies, as social and economic coordination, how markets are maintained and constructed, and concepts of fairness associated with markets and market allocation. The course ends with an examination of digital markets, blockchain technologies and ongoing concerns with digital antitrust and platform power.

Students will learn to critically examine and take a historical and theoretically informed view towards the role of markets. Students will be able to apply such framings to existing issues in digital markets, such as antitrust, control over market information, and price discrimination, across domains such as ad auctions, e-commerce and the gig economy.

This course is suitable for both graduate and undergraduate students interested in how markets are studied across different fields, and is designed to be accessible to students of all backgrounds.

This multidisciplinary course explores how product leaders lead product teams to build and launch successful products in market by drawing from multidisciplinary best practices across user research, design, and engineering. Topics will interweave new learnings with I School curriculum concepts to demonstrate how to apply these skills and work with relevant stakeholder experts at different stages of product development. Students are required to participate in weekly sessions and complete three module activities that will build up a digital portfolio that can be used in their job search after school. Students taking this course will further deepen their understanding of how the concepts they have learned in the I School curriculum are relevant to the field of product management by applying them first-hand.

This seminar is designed to allow students to explore the politics of information in greater depth than is possible in an introductory survey course. Each week, we will read and discuss a carefully selected, recent book on an issue in the field. Books offer greater analytical depth and complexity of insight into the issues. The topics include the origins of the internet, Section 230, social media and political polarization, the rise of surveillance advertising, what is privacy, privacy in practice, privacy by design, internet security, digital monopolies, and state control of information. A final week asks if/how society can rise to the challenges.

This course will explore what HCI knowledge and methods can bring to the study, design, and evaluation of AI systems with a particular emphasis on the human, social, and ethical impact of those systems. Students will read papers and engage in discussions around the three main components of a human-centered design process as it relates to an AI system:

1. needs assessment,
2. design and development, and
3. evaluation.

Following these three main design phases, students will learn what needs assessment might look like for designing AI systems, how those systems might be prototyped, and what HCI methods for real-world evaluation can teach us about evaluating AI systems in their context of use. The course will also discuss challenges that are unique to AI systems, such as understanding and communicating technical capabilities and recognizing and recovering from errors.

In this course you’ll learn industry-standard agile and lean software development techniques such as test-driven development, refactoring, pair programming, and specification through example. You’ll also learn good object-oriented programming style. We’ll cover the theory and principles behind agile engineering practices, such as continuous integration and continuous delivery.

This class will be taught in a flip-the-classroom format, with students programming in class. We'll use the Java programming language. Students need not be expert programmers, but should be enthusiastic about learning to program. Please come to class with laptops, and install IntelliJ IDEA community edition. Students signing up should be comfortable writing simple programs in Java (or a Java-like language such as C#).

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3D measurements from images, and modern deep-learning based techniques for image classification and recognition.

An intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc.). Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. Circulated material may include dissertation chapters, qualifying papers, article drafts, and/or new project ideas. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading.

Topics in information management and systems and related fields. Specific topics vary from year to year. May be repeated for credit, with change of content. May be offered as a two semester sequence.

The rise of misinformation and disinformation is challenging enough in a human-only world. Add new generative AI capabilities, large language models, synthetic media, and the splintering of even less moderated social media, and the problem seems almost insurmountable. In this class, we will survey the challenges in policy, technology, and research and then develop proposals for addressing these challenges.

Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree.