Information Course Schedule spring 2022

Upper-Division

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?

Section 1
TuTh 5:00 pm - 6:30 pm — Anthro/Art Practice Bldg 155
Instructor(s): Paul Duguid
Discussion Section 101
Fr 10:00 am - 11:00 am — 205 South Hall
Discussion Section 102
Fr 1:00 pm - 2:00 pm — 205 South Hall

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.

Section 1
Th 3:30 pm - 6:30 pm — 202 South Hall
Undergraduates interested in INFO 114/214 should sign up... more
Undergraduates interested in INFO 114/214 should sign up for the INFO 114 waitlist. A very LIMITED number of undergraduates will be enrolled into this course. Please have a back up class as it is highly likely you will not get in.

This course covers the application of 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 (IT) and IT industries. Topics include: economics of information; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.

Section 1
MoWe 2:00 pm - 3:30 pm — 205 South Hall
Instructor(s): John Chuang

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.

Section 1
TuTh 2:00 pm - 3:30 pm — 100 Lewis
Instructor(s): David Bamman
Please check back in PH2 if there is space on the waitlist... more
Please check back in PH2 if there is space on the waitlist. This class is extremely full and popular with limited space. Students should plan on a backup class if you cannot get into this course. Time conflicts for this version of the class will be allowed. The lecture for this class will be recorded via course capture and exams will be take-home. Questions regarding INFO 159 should be directed to studentaffairs@ischool.berkeley.edu.

Graduate

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.

Section 1
We 6:30 pm - 8:00 pm — Physics Building 2
Instructor(s): Michael Rivera

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.

Section 1
We 9:00 am - 11:00 am — 159 Mulford
Instructor(s): Morgan Ames

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.

Section 1
MoWe 12:00 pm - 2:00 pm — 105 North Gate
Instructor(s): Deirdre Mulligan
This class will be held from 12:30 - 2:00pm.
This class will be held from 12:30 - 2:00pm.

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.

Section 1
Th 3:30 pm - 6:30 pm — 202 South Hall
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"?
Section 1
Mo 3:30 pm - 6:30 pm — 205 South Hall
Instructor(s): Paul Duguid

"Behavioral Economics" is one important perspective on how information impacts human behavior. The goal of this class is to deploy a few important theories about the relationship between information and behavior, into practical settings — emphasizing the design of experiments that can now be incorporated into many 'applications' in day-to-day life. Truly 'smart systems' will have built into them precise, testable propositions about how human behavior can be modified by what the systems tell us and do for us. So let's design these experiments into our systems from the ground up! This class develops a theoretically informed, practical point of view on how to do that more effectively and with greater impact.

Section 1
TuTh 6:30 pm - 8:00 pm — 210 South Hall
Instructor(s): Neyat Daniel

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.

Section 1
TuTh 11:00 am - 12:30 pm — 210 South Hall
Instructor(s): Coye Cheshire

Three hours of lecture per week. Application of 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; economics of information goods, services, and platforms; strategic pricing; strategic complements and substitutes; competition models; network industry structure and telecommunications regulation; search and the "long tail"; network cascades and social epidemics; network formation and network structure; peer production and crowdsourcing; interdependent security and privacy.

Section 1
MoWe 2:00 pm - 3:30 pm — 205 South Hall
Instructor(s): John Chuang

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.

Section 1
Mo 5:30 pm - 7:00 pm — 202 South Hall
This is a flipped format class. Students will watch 1.5... more
This is a flipped format class. Students will watch 1.5 hours of asynchronous lecture PRIOR to attending the weekly in-person synchronous (live) lecture/discussion portion of the class. PREREQUISITES: INFO 271B or equivalent graduate-level coursework in statistics or econometrics are STRONGLY recommended prior to taking this course.

The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Three hours of lecture and one hour of laboratory per week.

Section 1
MoWe 11:00 am - 12:30 pm — Jacobs Hall 310
Instructor(s): Marti Hearst
Laboratory Section 101
Th 1:00 pm - 2:00 pm — Jacobs Hall 310
Instructor(s): Marti Hearst

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.

Section 1
TuTh 9:30 am - 11:00 am — 202 South Hall
Instructor(s): Joshua Blumenstock
Students can enroll in lecture 001 or 002 (same class and... more
Students can enroll in lecture 001 or 002 (same class and same day/time). The lecture will be live-streamed across classrooms.
Section 2
TuTh 9:30 am - 11:00 am — 210 South Hall
Instructor(s): Joshua Blumenstock
Students can enroll in lecture 001 or 002 (same class and... more
Students can enroll in lecture 001 or 002 (same class and same day/time). The lecture will be live-streamed across classrooms.
Section 101
We 2:00 pm - 3:00 pm — Physics Building 3

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.

Section 1
Mo 9:00 am - 11:00 am — 202 South Hall
Instructor(s): Kay Ashaolu
Laboratory Section 101
Fr 9:00 am - 10:00 am — 202 South Hall
Instructor(s): Kay Ashaolu

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.

Section 1
TuTh 2:00 pm - 3:30 pm — 100 Lewis
Instructor(s): David Bamman
Time conflicts for this version of the class will be... more
Time conflicts for this version of the class will be allowed. The lecture for this class will be recorded via course capture and exams will be take-home. Questions regarding INFO 259 should be directed to studentaffairs@ischool.berkeley.edu.

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.

Section 1
Fr 9:30 am - 12:30 pm — 210 South Hall
Instructor(s): Kimiko Ryokai

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.

Section 1
MoWe 11:00 am - 12:30 pm — 205 South Hall
Instructor(s): Jay Chen

New Venture Discovery introduces students to the process of launching an information-intensive venture — a social enterprise, business startup, or venture inside an established organization. It is motivated by the recognition that new enterprises fail more often from lack of customers than flaws in technology or product development. The course takes an iterative, design-oriented, and feedback-driven approach to the search process: identifying a problem or need to address, developing a prototype, discovering customers, refining the concept, testing and validating demand, and developing a sustainable business model.

Section 1
TuTh 11:00 am - 12:30 pm — 205 South Hall

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.

Section 1
MoWe 6:30 pm - 8:00 pm — Internet/Online
Instructor(s): Tiffany Rad
This class will start 2 weeks earlier than other Spring... more
This class will start 2 weeks earlier than other Spring 2022 Classes. 1st Live Session starts MONDAY January 3, 2022! These are online, LIVE sessions, where attendance is REQUIRED. This class is NOT on Berkeley Time.
Lecture Section 101
12:00 am - 12:01 am — Internet/Online
Instructor(s): Tiffany Rad
This class will start 2 weeks earlier than other Spring 2022 Classes. 1st Live Session starts MONDAY January 3, 2022! These are online, LIVE sessions, where attendance is REQUIRED. This class is NOT on Berkeley Time.

Will artificial intelligence technologies revolutionize warfare? Do cyberattacks represent an act of war? How do governments drive technological innovation in support of national security? What is the responsibility of the private sector when engaging in R&D with dual-use applications? To answer these questions, this course examines the intersection between politics, security, and technology both in the United States and across the globe.

The course is divided into four parts:

  1. Linking Politics, Security, and Technology in Theory
  2. Technology and War: A History
  3. Contemporary Debates: Today’s “Emerging” Technologies
  4. Tomorrow’s Technologies

Given the necessary breadth, our seminar will consider work from international relations, economics, science and technology studies, law, as well as non-academic writing in popular outlets. Topics include: offset strategies, offense-defense balance theory, grey-zone competition, conceptualizing strategic stability, escalation, the economics of industrial policy, and innovation policy. Technologies considered include: robotics, autonomous platforms (UAVs, UUVs), sensors for remote detection, machine learning, hypersonic missiles, missile defense technologies, and nuclear modernization.

Section 3
Fr 9:00 am - 12:00 pm — 107 South Hall
Instructor(s): Andrew Reddie

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.

Section 5
Tu 3:30 pm - 5:30 pm — 205 South Hall
Instructor(s): Laith Ulaby

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.

Section 4
Tu 3:35 pm - 6:06 pm — UC LAW 145
Instructor(s): Deirdre Mulligan
Completion of, or concurrent enrollment in, another class... more
Completion of, or concurrent enrollment in, another class on privacy from a legal or technological perspective is recommended. Attendance at the first class is required. 10 WEEK CLASS = Class Starts 1/18/2022 and Ends 3/29/2022

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.

Section 6
Tu 1:00 pm - 3:00 pm — 205 South Hall
Instructor(s): Niloufar Salehi
Prerequisite is INFO 217A. Interested students should... more
Prerequisite is INFO 217A. Interested students should waitlist and enrollment will be determined by the instructor.

This course will survey the myriad challenges facing climate mitigation and climate adaptation efforts, and explore ways in which the informatics and computing field can contribute to these efforts. Through readings, discussions, and group activities, participants of the reading group will critically engage with foundational and leading-edge perspectives on diverse topics such as systems thinking, multi-scale ecosystem monitoring, green IT and AI, decentralized climate finance, crisis and resilience informatics, and sustainability transition design.

Section 1
Tu 3:30 pm - 5:30 pm — 107 South Hall
Instructor(s): John Chuang

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.

Section 2
Mo 2:00 pm - 5:00 pm — 202 South Hall
Instructor(s): Daniel Aranki

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#).

Section 1
Fr 12:30 pm - 3:30 pm — 210 South Hall
Instructor(s): Jez Humble

This class will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and data preparation for 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.

The class will balance foundational concerns with exposure to practical languages, tools, and real-world concerns. We will study the foundations of prevalent data models in use today, including relations, tensors, and dataframes, and mappings between them. We will study SQL as a means to query and manipulate data at scale, including performance concerns like views and indexes, query processing and optimization, and transactions, all from a user perspective. We will study the foundations and realities of data preparation, including hands-on work with real-world data using standard Python and SQL frameworks. We will explore data exploration modalities for non-programmers, including the fundamentals behind spreadsheet systems and interactive visual analytics packages. We will look at approaches for managing the machine learning lifecycle of data preparation, model selection and training, model serving and monitoring. Time permitting we will look at technologies for moving, sharing, and caching data including event streaming systems, key-value/document stores, log analytics, and search engines.

Section 3
Fr 2:00 pm - 5:00 pm — 202 South Hall
COMPSCI C100/DATA C100/STAT C100 or COMPSCI189 or INFO 251... more
COMPSCI C100/DATA C100/STAT C100 or COMPSCI189 or INFO 251 or DATA 144/INFO 254 or equivalent upper-div course in data science. COMPSCI 61A or COMPSCI 88 or INFO 206B or equivalent courses in programming.

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.

Section 1
We 2:00 pm - 4:00 pm — 107 South Hall
Instructor(s): Coye Cheshire

One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Prerequisites: Ph.D. standing in the School of Information. Colloquia, discussion, and readings designed to introduce students to the range of interests of the school.

Section 1
Mo 10:00 am - 11:00 am — 205 South Hall
Instructor(s): Kimiko Ryokai
Tentatively scheduled. Day/time may change.
Tentatively scheduled. Day/time may change.

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.

Section 1
Fr 3:00 pm - 5:00 pm — 107 South Hall