Information Course Schedule spring 2018

Lower Division Courses

Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.

Also listed as Computer Science C8 and Statistics C8.

MWF 10:00 - 11:00 am | 150 Wheeler
Instructor(s): Anindita Adhikari

Upper Division Courses

According to conventional wisdom, the “information age” began just a few decades ago and promptly superseded everything that went before it. But the issues we are wrestling with now—questions about piracy, privacy, trust, “information overload,” and the replacement of old media by new—all have their roots in the informational cultures of earlier periods. In this class we will take a long view of the development of these cultures and technologies, from the earliest cave painting and writing systems to the advent of print, photography and the telegraph to the emergence of the computer and Internet and the world of Twitter, Pinterest and beyond. In every instance, be focused on the chicken-and-egg questions of technological determinism: how do technological developments affect society and vice-versa?

TuTh 9:30 - 11:00 am | 155 Kroeber
Instructor(s): Geoffrey Nunberg, Paul Duguid

Three hours of lecture per week. With the advent of virtual communities and online social networks, old questions about the meaning of human social behavior have taken on renewed significance. Using a variety of online social media simultaneously, and drawing upon theoretical literature in a variety of disciplines, this course delves into discourse about community across disciplines. This course will enable students to establish both theoretical and experiential foundations for making decisions and judgments regarding the relations between mediated communication and human community.

Students will receive no credit for Sociology C167/Information C167 after taking Sociology 167.

Also listed as Sociology C167.

TuTh 11:00 am - 12:30 pm | 2050 Valley Life Sciences
Instructor(s): Jill A. Bakehorn

This course provides an introduction to ethical and legal issues surrounding data and society, as well as hands-on experience with frameworks, processes, and tools for addressing them in practice. It blends social and historical perspectives on data with ethics, law, policy, and case examples — from Facebook’s “Emotional Contagion” experiment to controversies around search engine and social media algorithms, to self-driving cars — to help students develop a workable understanding of current ethical and legal issues in data science and machine learning. Legal, ethical, and policy-related concepts addressed include: research ethics; privacy and surveillance; bias and discrimination; and oversight and accountability. These issues will be addressed throughout the lifecycle of data — from collection to storage to analysis and application. The course emphasizes strategies, processes, and tools for attending to ethical and legal issues in data science work. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.

Student Learning Outcomes: Upon completion of the course, students will be able to critically assess their own work and education in the area of data science; to identify and articulate basic ethical and policy-based frameworks; and to understand the relationship between their work and ethical frameworks and legal obligations.

Section: 2
TuTh 12:30 - 2:00 pm | 202 South Hall
Instructor(s): Deirdre Mulligan

Data Mining and Analytics introduces students to the practical fundamentals and emerging paradigms of data mining and machine learning with enough theory to aid intuition building. The course is project oriented, with a project beginning in class every Thursday and to be completed outside of class by the following week, or two for longer assignments. The in-class portion of the project is meant to be collaborative and a time for the instructor and GSIs to work closely with project groups to understand the objectives, help  work through software logistics, and connect project work to lecture. Tuesday lectures introduce theories, concepts, contexts and algorithms. Students should expect to leave the class with hands-on, contemporary data mining skills they can confidently apply in research and industry. There will be a written midterm test and a final group project report and presentation. Experience with Python is required.

Learning Objectives

  • Foster critical thinking about real world actionability from analytics.

  • Develop intuition in various machine learning classification algorithms (e.g., decision trees, feed-forward neural networks, recurrent neural networks, support vector machines) and clustering techniques (e.g., k-means, spectral, skip-gram)

  • Conduct manual feature engineering (from domain knowledge) vs. machine induced featurization (representation learning)

  • Provide an overview of issues in research and practice that will affect the practice of data science in a variety of domains.

For graduate students, this course is listed as Info 254.

Section: 1
TuTh 2:00 - 3:30 pm | 145 Moffitt Library
Instructor(s): Zachary Pardos

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.

This course is also offered for graduate students as Info 234.

Section: 3
MW 2:00 - 3:30 pm | 202 South Hall
Instructor(s): John Chuang

Core Courses

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 is a half-semester course, and is offered during the second half of the semester.

8 weeks - 3 hours of lecture per week

NOTE: Before Fall 2016, this course was named Social and Organizational Issues of Information. The course was offered for 3 units in Spring 2010 and Spring 2011 and for 4 units from 2012 to 2017.

TuTh 3:30 - 5:00 pm (Mar 13 – May 10) | 202 South Hall
Instructor(s): Jenna Burrell

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 is a half-semester course, and is offered during the first half of the semester.

7 weeks - 4 hours of lecture per week.

NOTE: Between 2011 and 2017, this course was offered for 3 units.

TuTh 3:30 - 5:00 pm (Jan 16 – Mar 8) | 202 South Hall
Instructor(s): Deirdre Mulligan

General Courses

This course addresses concepts and methods of user experience research. The emphasis will be on methods of collecting and interpreting many kinds of data about real-world user activities and practices and translating them into design decisions. The course includes hands-on practice with a number of major user experience research methods, including heuristic evaluation; observation; interviews, surveys and focus groups. The emphasis will be on naturalistic/ethnographic (qualitative) methods, but we will also address major quantitative methods. Finally, we will discuss methods of bringing user experience research into the design process.

This course is appropriate for both 1st and 2nd-year MIMS students, and for students from other departments with a strong interest in user experience research, with the instructor's permission. Students will complete at least one major group project related to needs assessment and evaluation. Second-year MIMS students may use this project to meet their capping project requirement.

Th 12:30 - 3:30 pm | 210 South Hall
Instructor(s): Steve Fadden

“Information” is a versatile word. It’s the name we attach to the age we live in, to and the technologies that define it, to the society and economy that they give rise to, and to the "revolution" that these technologies launch. It characterizes a variety of professions, activities, and social conditions (information architect, CIO, information overload, information haves and have-nots, information warfare), and not incidentally the new faculties that take “information” as their unifying focus. The word figures as a theoretical or technical term in a number of disciplines, including AI, computer science, philosophy, psychology, linguistics, economics, political science and information theory. In short, the word stands (along with its sister “data”) for a welter of social, technological and intellectual connections that seem to define a large swath of modern life.

In this class, we will not be trying to define “information” or “data” (though we’ll look at some attempts to do so). Rather we want to take the word as a point of entry to explore the connections and ideologies that it evokes. Why do people assume, for example, that the bits and bytes sitting on their hard drives are the same as the stuff that creates social revolutions and whose free exchange is necessary to the health of democratic society? (Would we make those connections if we didn’t use the word “information” to describe them?) How are the notions of information deployed by management science or artificial intelligence connected to the information theory developed by Shannon?

We’ll be taking on these questions by discussing readings both from historical periods and from a range of disciplines, focusing on the some of notions (such as “information,” “data,” “platform,” “technology,” “knowledge”) that seem to connect them.

Th 12:30 - 3:30 pm | 205 South Hall
Instructor(s): Geoffrey Nunberg, Paul Duguid

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.

This course is also offered for undergraduate students as Info 190.

MW 2:00 - 3:30 pm | 202 South Hall
Instructor(s): John Chuang

Information visualization is widely used in media, business, and engineering disciplines to help people analyze and understand the information at hand. The industry has grown exponentially over the last few years. As a result there are more visualization tools available, which have in turn lowered the barrier of entry for creating visualizations.

This course provides an overview of the field of Information Visualization. It follows a hands-on approach. Readings and lectures will cover basic visualization principles and tools. Labs will focus on practical introductions to tools and frameworks. We will discuss existing visualizations and critique their effectiveness in conveying information. Finally, guest speakers from the industry will give an insight into how information visualization is used in practice.

All students are expected to participate in class discussion, complete lab assignments, and create an advanced interactive data visualization as a semester project.

Priority for attending this class is given to I School students. The semester project involves programming; therefore students are expected to have some coding experience. Interested students from other departments are invited to join the class if they can demonstrate the required skills.

Note: This course is offered for a letter grade only.

Note: Until 2014, this course was offered for 3 units.

MW 10:30 am - 12:00 pm | 202 South Hall
Instructor(s): Marti Hearst

Data Mining and Analytics introduces students to the practical fundamentals and emerging paradigms of data mining and machine learning with enough theory to aid intuition building. The course is project oriented, with a project beginning in class every Thursday and to be completed outside of class by the following week, or two for longer assignments. The in-class portion of the project is meant to be collaborative and a time for the instructor and GSIs to work closely with project groups to understand the objectives, help  work through software logistics, and connect project work to lecture. Tuesday lectures introduce theories, concepts, contexts and algorithms. Students should expect to leave the class with hands-on, contemporary data mining skills they can confidently apply in research and industry. There will be a written midterm test and a final group project report and presentation. Experience with Python is required.

Learning Objectives

  • Foster critical thinking about real world actionability from analytics.

  • Develop intuition in various machine learning classification algorithms (e.g., decision trees, feed-forward neural networks, recurrent neural networks, support vector machines) and clustering techniques (e.g., k-means, spectral, skip-gram)

  • Conduct manual feature engineering (from domain knowledge) vs. machine induced featurization (representation learning)

  • Provide an overview of issues in research and practice that will affect the practice of data science in a variety of domains.

Previously offered as Info 290T. Data Mining and Analytics.

For undergraduate students, this course is also offered as Info 190.

TuTh 2:00 - 3:30 pm | 145 Moffitt Library
Instructor(s): Zachary Pardos

Three hours of lecture per week. This course is concerned with the use of Database Management Systems (DBMS) to solve a wide range of information storage, management and retrieval problems, in organizations ranging from large corporations to personal applications, such as research data management. The course combines the practical aspects of DBMS use with more theoretical discussions of database design methodologies and the "internals" of database systems.

A significant part of the course will require students to design their own database and implement it on different DBMS that run on different computer systems. We will use both ACCESS and ORACLE.

In the theoretical portion of the course, we will examine the major types or data models of DBMS (hierarchical, network, relational, and object-oriented). We will discuss the principles and problems of database design, operation, and maintenance for each data model.

F 2:00 - 5:00 pm | 202 South Hall
Instructor(s): Kay Ashaolu

How does good design enhance or facilitate interaction between people? How does good design make the experience people have with computational objects and environments not just functional, but emotionally engaging and stimulating? This semester seminar 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 discussions, in addition to lectures and readings.

Students will receive no credit for C265 after taking 290 section 6 (Spring 2009 or Fall 2010; New Media 290 section 1 (Spring 2009) or New Media 290 section 2 (Fall 2010).

Also listed as New Media C265.

F 9:30 am - 12:30 pm | 210 South Hall
Instructor(s): Kimiko Ryokai

Special Topics Courses

This one-credit reading group, sponsored by the Center for Long-Term Cybersecurity, will discuss contemporary cybersecurity policy problems. The seminar will focus on future trends in technology, as well as the economy and politics, and how those are affecting cybersecurity policy. Topics may include encryption, autonomous vehicles, and the ethics of artificial intelligence. Students would be required to attend weekly 50-minute sessions, present short papers on the readings, and write response pieces.

Section: 5
Tu 1:00 - 2:30 PM | 210 South Hall
Instructor(s): Chris Jay Hoofnagle

Description unavailable.

This is a five-week class.

Section: 2
Tu 11:00 am - 2:00 pm | 205 South Hall (Jan 16 – Feb 20)
Instructor(s): Michael Koved

Description TBA

Section: 7
Tu 5:00 - 7:00 pm | 107 South Hall
Instructor(s): AnnaLee Saxenian Dacher Keltner

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.

Section: 3
TuTh 11:20 am - 12:40 pm | 1535B Tolman
Instructor(s): Chris Jay Hoofnagle

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.

Section: 1
Mon 4:00 - 5:00 pm | 107 South Hall
Instructor(s): John Chuang

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

Course Prerequisites

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.

Section: 4
Tu 2:30 - 5:30 pm | 205 South Hall
Instructor(s): Marti Hearst

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
F 10:00 am - 1:00 pm | 202 South Hall
Instructor(s): Jez Humble

This is a hands-on full-stack web development course, and students will work on all aspects of the full-stack web development process. Individual and team assignments will enable students to develop skills in data modeling, database and API design, responsive front-end design, version control, and deployment using Python, JavaScript, and full-stack frameworks such as Flask. The goal of this course is to help students understand different technologies and work towards being able to implement complete web-based projects for desktop and mobile.

Section: 2
Th 6:00 - 9:00 pm | 202 South Hall

Seminars & Colloquia

One hour colloquium per week. Must be taken on a satisfactory/unsatisfactory basis. Colloquia, discussion, and readings are designed to introduce students to the range of interests of the school.

W 11:00 am - 12:00 pm | 107 South Hall
Instructor(s): Paul Duguid

The seminar explores selected advanced topics relating to 'digital libraries' with special emphasis on:

  • Access to networked resources
  • Use of two or more resources in conjunction
  • Combined use of two or more retrieval systems (e.g. use of pre- or post-processing to enhance the capabilities)
  • The redesign of library services

It is expected that these issues will require attention to a number of questions about the nature of information retrieval processes, the feasibility of not-yet-conventional techniques, techniques of making different systems work together, social impact, and the reconsideration of past practices. More generally, the seminar is intended to provide a forum for advanced students in the School. Anyone interested in these topics is welcome to join in — and to talk about their own work. This is a continuation of the previous Lynch/Buckland seminars.

Section: 1
F 3:00 - 5:00 pm | 107 South Hall