Special Topics in Information
Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.
In this class students will continue research projects from INFO 217A. HCI research. The class includes weekly one-on-one meetings with each project team. Students will read literature related to their project assigned by the instructor and continue their projects. The final deliverable for the class will be a full conference or journal paper.
This course will investigate the unique challenges and opportunities encountered when applying artificial intelligence (AI) to healthcare needs. It will present techniques of AI used in medicine for disease diagnosis, prevention, and treatment. Students from a variety of backgrounds are welcome! In addition to technical aspects of designing AI solutions, we will explore social, ethical, and health policy considerations. Class time will take a variety of formats, including lectures, open discussions, student presentations, and case studies. Final deliverable for the course will be a written report or computer program. The only prerequisites are an interest in Healthcare AI and a willingness to engage in interdisciplinary study; no programming experience is necessary. The curriculum is a journey through Artificial Intelligence (AI) technologies, as they relate to the Healthcare space (HAI). We will look at predictive models, using electronic health records as a data source, and other topics, and conclude with predictions for the future and understanding emerging trends.
This course is designed to act as a bridge between the understanding of irrational human behavior and its application to real-world design problems. In this class, students will learn to approach product design problems through behavioral economics framework. Using a simple iterative approach for understanding and finding target users and behaviors, they will learn how to develop effective interface designs and build products. Drawing upon our industry experience, the class will follow lean and agile methods such as drafting user flows and identifying obstacles to changing behavior. Alumni from different backgrounds would be invited to talk about their experience tackling behavioral design problems at work. This class is aimed at students interested in product design and product management but anyone with an interest in building modern software systems would benefit from this experience.
Biosensory computing is the multidisciplinary study and development of systems and practices that sense, represent, communicate, and interpret biological signals from the body.
Biosignals are expansive in scope, and can enable a diverse range of biosensory computing applications. They can include physiological (e.g., ECG/PPG, EDA, EEG) and kinesthetic signals (e.g., accelerometry, eye gaze, facial expressions). Many inferences can be drawn about the person from these signals, including their activities, emotional and mental states, health, and even their identities, intentions, memories, and thoughts.
While generated by the person, biosensory data have important characteristics that distinguish them from other types of user-generated data. They are intimate yet leakable, precise yet ambiguous, familiar yet unverifiable, and have limited controllability. Therefore, responsible stewardship of biosensory data must be in place before the full potential of biosensory computing can be realized.
This multidisciplinary course will explore the intellectual foundations and research advances in biosensory computing. We will survey the range of biosensing modalities and technologies, study temporal and spectral data analysis and visualization techniques, interrogate the designs of novel biosensing applications, and tackle issues of user privacy and research ethics. Students signing up for the 3-unit option will continue in the second half of the semester with a student-led research project.
This course will explore how legal, ethical, and economic frameworks enable and constrain security technologies and policies. As digital technologies penetrate deeply into almost every aspect of human experience, a broad range of social-political-economic-legal-ethical-military and other non-technical considerations have come to envelope the cybersecurity landscape. Though cybersecurity itself is a technical discipline, these non-technical considerations constrain it, enable it, and give it shape. We will explore the most important of these elements. The course will introduce some of the most important macro-elements (such as national security considerations and the interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking (on the national, international, and organizational level), business models, legal frameworks (including duties of security, privacy issues, law enforcement access issues, computer hacking, intellectual property, and economic/military/intellectual property espionage), standards making, and the roles of users, government, and industry.
Data and the algorithmic systems are ubiquitous in everyday life. These data encode our daily choices, actions, and behaviors, as well as our more persistent social identities. They also enrich the lives of some while limiting the life chances of others. In this way, data generated and collected about us form a type of information infrastructure: pervasive, hidden, and at times insidious. As technology and data-driven systems increasingly enter into our public, professional, and personal spheres, more of these worlds become encoded in data and result in shifts in the power relations within those worlds. In a word, data is a medium which reconfigures power.
In this seminar, we will engage readings around data, power, and infrastructure, drawing from a number of interdisciplinary academic, artistic, and activist traditions. We’ll discuss topics related to state projects of legibility and quantification; the genealogy of the modern data subject; the politics of classification systems; the surveillance of Blackness and the carceral logics of technology; administrative violence and trans and gender non-conforming identities; the invisible labor powering data-driven systems; and the resistances, obfuscations, and refusals to datafication and surveillance.
Many products of human invention — political speeches, product reviews, status updates on Twitter and Facebook, literary texts, music and paintings — have been analyzed, not uncontroversially, as “data”. In this graduate-level course (open to all departments, especially those in the humanities and social sciences), we will pursue two ends: we will investigate the landscape of modern quantitative methods for treating data as a lens onto the world, surveying a range of methods in machine learning and data analysis that leverage information produced by people in order to draw inferences (such as discerning the authorship of documents and the political position of social media users, charting the reuse of language in legislative bills, tagging the genres of songs, and extracting social networks from literary texts). Second, we will cast a critical eye on those methods, and investigate the assumptions those algorithms make about the world and the data through which we see it, in order to understand their limitations and when to apply them. How and when can empirical methods support other forms of argumentation, and what are their limits? Many of these techniques are shared among the nascent communities of practice known as “computational social science”, “computational journalism” and the “digital humanities”; this course provides foundational skills for students to conduct their own research in these areas. No computational background is required.
This is an introductory course on design, problem solving and innovation. While the principles generalize to any context, this course focuses on solutions that take the form of digital goods and services.
This is a team-based, experiential learning course. Students who take this course should expect to:
Work with a team that includes different backgrounds, interests, and personal motivations. As a cross-listed course, teams may or may not include students from different schools across the University (depending upon enrollment).
Experience a process for identifying and prioritizing opportunities to innovate. The process scales from an entrepreneur working alone to Fortune 500 firms managing an innovation portfolio.
Practice applying qualitative processes (including customer interviews, paper prototyping, and remote user-testing) to characterize the "job to be done," isolate a "minimum viable problem," and iterate your design prototypes.
Practice applying quantitative processes (including analysis of keyword searches, digital ad campaigns, and funnel analysis) to characterize the "job to be done," isolate a "minimum viable problem," and iterate your design prototypes.
Formulate hypotheses and then design and execute experiments in a Lean cycle of build, measure and learn.
Teams will learn general principles of product/service design in the context of tools, methods, and concepts specific to the Web-based environment. Both desktop and mobile products and services are prototyped in the Web context to leverage common development and testing resources. For purposes of the course, the product or service should be aimed at consumers in the range 25 - 45. We define this target audience so that we can use classmates as preliminary subjects of interviews, testing, and surveys. For the purposes of this course, the product or service need not have a compelling business model. The focus is on creating a product or service that solves a real problem, not necessarily creating a new business.
This course teaches a process-oriented approach to product and service design with heavy emphasis on user experience design. Students interested in design aesthetics, semiotics and cognitive psychology should look elsewhere. Neither is this a class about technology. The course syllabus does not include tutorials on specific software packages. Students interested in technical questions such as platform selection and scaling should look elsewhere.
This course introduces students to experimentation in the social sciences. 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 have facilitated the development of better data gathering. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data.
Ethnographic research provides rich detail about a given slice of the social world, providing a nuanced analysis of culture, context, and how people collectively make sense of their daily lives. These inductive and immersive methods can answer questions about shared experiences and perceptions and mechanisms of change, and provide the grounds for generating testable hypotheses. In recent years, ethnographic methods have further expanded in response to challenges from feminist, post-colonial, and anti-racist movements and in recognition of shifting socio-technical assemblages enabled by digital networks and platforms. A number of scholars have taken up the call for reflexive and flexible approaches to studying community life in the digital age, across a range of social spheres. In this course, we will dive into this robust body of recent scholarship and explore what it can teach us about how the digital shapes and is shaped by our social ecosystems.
This class is ideal for graduate students and advanced undergraduates interested in qualitative methods in information studies, including but not limited to those in information,sociology, anthropology, geography, gender and ethnic studies, and other interdisciplinary programs.
For firms and organizations that handle personal data, the desire to extract valuable information and insight must be balanced against the privacy interests of individuals. This task has grown considerably harder in the last few decades, with the development of advanced learning algorithms that can leverage statistical patterns to infer personal information. As a result, databases that were recently considered anonymized have been proven vulnerable to attack. Starting with the seminal definition of differential privacy, researchers are now responding with a new generation of algorithmic techniques, based on strong adversary models and offering mathematical bounds on worst-case privacy loss. This course is an introduction to the field known as formal privacy or differential privacy. It includes both foundational theory and algorithmic techniques for building private algorithms. A particular focus is placed on algorithms for statistical learning, and to research that incorporates a statistical perspective.
The first third of the course is structured like a bootcamp, with problem sets to build fluency in the most common mathematical structures used in the field. The latter two-thirds of the course is structured like a research seminar, with student-led discussion of published articles each week. The course completes with a final research project, giving students a chance to develop new algorithms, extend theoretical results, or build systems that incorporate formal privacy guarantees.
The ability to manipulate, explore, and analyze structured data sets is foundational to the modern practice of data science. This course introduces students to data analysis using the Python programming language, especially the core packages NumPy and pandas. Student learn to operate on data, think critically about features they uncover, and organize results into a persuasive analysis. Best practices for writing code in a functional style are emphasized throughout the course. A set of weekly programming assignments reinforces and builds upon the techniques presented in lecture. The course culminates in a final project in which students write a professional quality analysis based on their own research questions.
This course forms the second half of a sequence that begins with INFO 206. It may also be taken as a stand-alone course by any student that has extensive Python experience.
The Future of Cybersecurity Reading Group (FCRG) is a two-credit discussion seminar focused on cybersecurity. In the seminar, graduate, professional, and undergraduate students discuss current cybersecurity scholarship, notable cybersecurity books, developments in the science of security, and evolving thinking in how cybersecurity relates to political science, law, economics, military, and intelligence gathering. Students are required to participate in weekly sessions, present short papers on the readings, and write response pieces. The goals of the FCRG are to provide a forum for students from different disciplinary perspectives to deepen their understanding of cybersecurity and to foster and workshop scholarship on cybersecurity.
In this group study class, we will cover the material in Data 8 using the online Data 8X a three-part professional certificate program in data science from UC Berkeley. This first course, “Computational Thinking with Python,” focuses on programming and data visualization. The second course, “Inferential Thinking by Resampling,” will focus on statistical inference. The third course is “Prediction and Machine Learning.”
This group study is intended for graduate students in professional schools who seek an introduction to data science in order to integrate techniques into their domain or to pursue further educational opportunities such as the graduate certificate in applied data science. The class format is essentially self-guided: students will watch the video lecture and complete the assignments before class, and then meet to discuss the lesson. Undergraduate assistants from Data 8 will coach class participants as necessary. There are small class projects that allow students to work with their own datasets.
Data 8X is based on a rigorous first-year undergraduate course at UC Berkeley called Foundations of Data Science. Over 1,000 students take this course each semester. The course is designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines, and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.
No prior programming experience is necessary, but many of the programming techniques covered in this course do not appear in a typical introduction to programming. The programming content of this course focuses on manipulating data tables, rather than building software applications. Therefore, students who take the course after taking other programming courses often learn a new approach to programming that they haven't encountered before.
Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.
This course surveys privacy mechanisms applicable to systems engineering, with a particular focus on the inference threat arising due to advancements in artificial intelligence and machine learning. We will briefly discuss the history of privacy and compare two major examples of general legal frameworks for privacy from the United States and the European Union. We then survey three design frameworks of privacy that may be used to guide the design of privacy-aware information systems. Finally, we survey threat-specific technical privacy frameworks and discuss their applicability in different settings, including statistical privacy with randomized responses, anonymization techniques, semantic privacy models, and technical privacy mechanisms.
Marketers want to deliver timely and relevant messages to their customers in support of brand building, acquisition, cross-sell, and retention. Though there are a wide array of channels, tools, and technologies available to multi-channel, multi-product marketers, the path to success is not an easy one.
The most formidable challenges include:
- What Are the Delivery Tools and Technologies Available to Marketers?
- Where and How to Spend Marketing Dollars Most Effectively?
- What Metrics Should Be Set to Gauge Success?
- What Data Are Available to and Generated by the Ecosystem?
The tools, metrics, and data used to execute and evaluate marketing spend can be described as the marketing analytics “ecosystem.” A common industry term is the “marketing technology stack.”
This class will provide a topical overview to the ecosystem and by the end of the class, have an understanding the connectivity between the marketing technology stack, the data utilized, data generated and useful metrics. This background is essential for students interesting in how marketing can drive successful outcomes for customers and for the business.
This course explores current debates about government regulation of online businesses. We start by examining the unintended consequences of digital advertising models that support many large online companies. We then review debates over antitrust, mis- and dis-information, privacy, content controls (e.g. pornography), and section 230 of the Communications Decency Act. The primary focus of the class is on US policy, but we will examine the EU's General Data Protection Regulation (GDPR), the most significant data protection legislation to date. We also look briefly at the way that these issues are being addressed elsewhere in the world and discuss the challenge of national regulation of global businesses.
This course covers the fundamental data structures and algorithms found in many technical interviews. These data structures include (but are not limited to): lists, stacks, queues, trees, heaps, hashes, and graphs. Algorithms, such as those for sorting and searching, will also be covered, along with an analysis of their time and space complexity. Students will learn to recognize when these data structures and algorithms are applicable, implement them in a group setting, and evaluate their relative advantages and disadvantages.
There is a burgeoning market for technologists and lawyers who can understand the application and implementation of privacy and security rules to network connected services. Privacy and Security Lab is a new course designed to promote the development of such “privacy technologists.” Students will meet twice a week, once in lecture, and the second time in a computer lab to gain hands-on skills in privacy and security analysis. The course will explore the concepts, regulations, technologies, and business practices in privacy and security, including how different definitions of “privacy” may shape technical implementation of information-intensive services; the nature of privacy and security enhancing services; and how one might technically evaluate the privacy and security claims made by service providers. There are no prerequisites and enrollment is open to law students to encourage cross-disciplinary exchanges.
Privacy counseling and compliance is a rapidly growing and increasingly important function, both within companies and throughout the legal profession. The task is becoming evermore complex as companies grapple with adherence to new legislation and regulation, as well as local and international standards and norms. This interdisciplinary course seeks to help prepare students for this changing ethical, legal, and regulatory landscape. The academic perspective will be grounded in a real world examination of compliance challenges which will be presented by leading privacy professionals including in-house legal and compliance experts.
This course gives participants hands-on software product design experience based on current industry practice. The course is project-based with an emphasis on iteration, practice, and critique from experienced industry designers. During the course, participants work iteratively on a series of design projects (both solo and in groups) through a full design process, including developing appropriate design deliverables and gathering feedback. We’ll also cover specific topics, including design and prototyping tools, working with and developing design systems, typical phases and deliverables of the design process, and designing in different contexts (e.g. startups vs. larger companies). There will also be guest lectures from industry experts.
This seminar will discuss topics of current interest in the multi-disciplinary field of ubiquitous sensing. The format will include paper discussions, invited lectures from both within and outside the class, and short written assignments. Students will also be responsible for presenting during at least one class session, either on their own research and ideas or on a selected set of papers relevant to the course topic.
Many of us are interested in looking forward towards future challenges and opportunities (near, medium, and occasionally long term) of the information economy and society. But technology prognostication has a terrible track-record. And keying on worst-case and best-case possibilities is an unrealistic, inefficient, and sometimes dangerous way to generate insight. Scenario thinking is an alternate methodology, developed first by Royal Dutch Shell for use in the energy sector after the oil shocks of the 1970s and later extended more broadly to business, government, and non-profit sectors. Scenario thinking starts from the proposition that the future is unpredictable in any meaningful sense… and that it is possible instead to systematically develop a landscape of possible futures from which useful insights can be drawn, and against which strategic action can be planned. In this seminar we will learn, practice, and develop scenario thinking for the information economy and society. We’ll explore the scientific limits of prediction; decision biases in that setting; and alternative methods for gaining and communicating insight that changes what people think and what they do. We’ll develop our own scenarios and use them to explore systematically challenges and opportunities ahead for the things we care about — business ideas, governance challenges, social change, etc. This seminar will call on a high level of energy, creativity, and open-mindedness as well as great teamwork.
Course Objective: Develop new ideas and technology for making a quantum leap in improving how people learn.
This is an interdisciplinary graduate research seminar whose goal is to design technology and learning practices that will make major, significant improvements over how learning and teaching are done today. The course will have a technology-centered focus, but the most important metrics will be those related to learning gains.
As this is a graduate seminar, students will be responsible for selecting and designing the materials and the presentations in the course, with only light supervision by the instructor.
Students earning 1 unit will do the following:
- Summarize current research papers and book chapters
- Complete paper and artifact evaluations before each class
- Complete in-class assignments, including peer-assessments
- Present information clearly and concisely
- Lead class sessions
Students earning 3 units will do the following:
- The work listed above for 1 unit, and:
- Innovate in one particular area of research
- Design, implement, and release a research artifact; one of
- Working with a team to engineer something great
- Writing a research paper proposing a future approach based on a detailed analysis of existing approaches
Ph.D. students who have an interest in pushing the state of the art in education and educational technology are the intended participants of this course. It is preferred if students already have some background in learning sciences, but not required. It is also preferred that students have programming background, but also not required, if instead they come from learning sciences or some other relevant non-CS field such as psychology. The same applies to master’s students.
Undergraduates will be accepted to the course if they can demonstrate a proven interest in the topic, relevant background, and can present a recommendation from a UC Berkeley professor or equivalent. (Having taken a course with the instructor is equivalent.) Interested undergraduates should email the instructor with the name of the professor to contact for their reference, and should also include a copy of the UC Berkeley transcript and their resume.
This course takes a multi-disciplinary approach to explore the possibilities and limitations of ubiquitous sensing technologies for physiological and contextual data. We will survey the intellectual foundations and research advances in ubiquitous computing, biosensory computing, and affective computing, with applications ranging from brain-computer interfaces to health and wellness, social computing to cybersecurity. We will cover temporal and spectral analysis techniques for sensor data. We will examine data stewardship issues such as data ownership, privacy, and research ethics. Students signing up for the 3-unit option will continue in the second half of the semester with a student-led research project.
This experiential course provides a framework for creating and managing a startup.
Creating a startup
Students will work in teams of 3 to develop an idea that we will work through over the course of the semester. If students are currently working with a startup, they can use that startup for the process as well. We will focus on the business model canvas as a tool to frame product-market fit and teams will be expected to conduct approx 100 stakeholder interviews over the course of the semester.
Managing a startup
Startups are often faced with resource shortages and overwhelmed with work. We will focus on decision making tools to manage both small issues as well as major pivots in product/service strategy to help bring structure to chaos. The course will cover a mix of tools to do this in the areas of project management and problem solving.
How do you create a concise and compelling User Experience portfolio? Applying the principles of effective storytelling to make a complex project quickly comprehensible is key. Your portfolio case studies should articulate the initial problem, synopsize the design process, explain the key decisions that moved the project forward, and highlight why the solution was appropriate. This course will include talks by several UX hiring managers who will discuss what they look for in portfolios and common mistakes to avoid.
Students should come to the course with a completed project to use as the basis for their case study; they will finish with a completed case study and repeatable process. Although this class focuses on UX, students from related fields who are expected to share examples and outcomes of past projects during the interview process (data science, product management, etc.) are welcome to join.