Information Course Schedule fall 2017

Upper-Division

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.

TuTh 3:30 - 5:00PM — 2060 Valley Life Sciences Building
Instructor(s): David Bamman

This course introduces students to data visualization: the use of the visual channel for gaining insight with data, exploring data, and as a way to communicate insights, observations, and results with other people.

The field of information visualization is flourishing today, with beautiful designs and applications ranging from journalism to marketing to data science. This course will introduce foundational principles and relevant perceptual properties to help students become discerning judges of data displayed visually. The course will also introduce key practical techniques and include extensive hands-on exercises to enable students to become skilled at telling stories with data using modern information visualization tools.

Students will be asked to complete assignments before class, work together in small groups in class, and provide peer assessments. Grades will be based on assignments, quizzes, in class participation, peer assessment quality, 2 midterms, and a final project. The assignments for the course will together work towards building a coherent visualization that tells a story and is visible on the web.

Prerequisites

This course is designed for upper division undergraduates who have an interest in design and in data. It is intended to accommodate students who have only a limited programming background, as well as those who are skilled with programming. For this reason, the only prerequisite is CS/Stat/Info 8 or equivalent. This course assumes students already have familiarity with basic data analysis and manipulation, and basic statistics.

Students are encouraged but not required to have taken other courses from the introductory design sequence (one of DES INV 10- Discovering Design DES INV 15- Design Methodology, DES INV 21- Visual Communications & Sketching, CS 160 User Interface Design and Development), as well as other introductory data science and statistics courses.

Graduate students will be accommodated only as space permits.

For Computer Science Students

Those students from Computer Science who would prefer a programming component, and who would like to receive technical course requirement credits for this course should enroll in a 1 unit optional companion course that is being offered alongside this course. This companion course will teach JavaScript and d3.js for information visualization applications.

Instructor

The course instructor is Professor Marti Hearst, one of the founders of the IEEE Infovis Conference. She is also a leader in the latest wave of innovations in teaching data visualization, and co-organized high-profile events on this topic at IEEE Infoviz 2015, 2016, and 2017. Prof. Hearst is internationally known for her research in user interfaces for search and text visualization, having recently been inducted into the ACM CHI Academy.

Section 1
MW 10:30AM - 12:00PM — 210 South Hall
Instructor(s): Marti Hearst

A seminar focusing on topics of current interest. Topics will vary. A seminar paper will be required. Open to students from other departments.

Section 2
M 6:00PM - 8:00PM — 210 Jacobs Hall
Instructor(s): Chris Henrick

Core

8 weeks; 3 hours of lecture per week. This course introduces the intellectual foundations of information organization and retrieval: conceptual modeling, semantic representation, vocabulary and metadata design, classification, and standardization, as well as information retrieval practices, technology, and applications, including computational processes for analyzing information in both textual and non-textual formats.

TuTh 11:00AM - 12:30PM (Course Dates: 10/19 - 12/15) — 210 South Hall
Instructor(s): David Bamman

7 weeks - 4 hours of laboratory per week. This course introduces software skills used in building prototype scripts for applications in data science and information management. The course gives an overview of procedural programming, object-oriented programming, and functional programming techniques in the Python scripting language, together with an overview of fundamental data structures, associated algorithms, and asymptotic performance analysis. Students will watch a set of instructional videos covering material and will have four hours of laboratory-style course contact each week.

TuTh 11:00AM - 12:30PM (Course Dates: 8/24 - 10/17) — 210 South Hall
Instructor(s): Niall Keleher

General

Three hours of lecture per week. User interface design and human-computer interaction. Examination of alternative design. Tools and methods for design and development. Human computer interaction. Methods for measuring and evaluating interface quality.
F 12:00 - 3:00PM — 210 South Hall
Instructor(s): Robert Youmans

Three hours of lecture per week. Policy and technical issues related to insuring the accuracy and privacy of information. Encoding and decoding techniques including public and private key encryption. Survey of security problems in networked information environment including viruses, worms, trojan horses, Internet address spoofing.

MW 2:00 - 3:30PM — 202 South Hall
Instructor(s): Doug Tygar

Three hours of lecture per week. This course focuses on managing people in information-intensive firms and industries, such as information technology industries. Topics include managing knowledge workers; managing teams (including virtual ones); collaborating across disparate units, giving and receiving feedback; managing the innovation process (including in eco-systems); managing through networks; and managing when using communication tools (e.g., tele-presence). The course relies heavily on cases as a pedagogical form.

Th 6:00 - 9:30 pm — C125 Cheit Hall
Instructor(s): Morten Hansen

"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.

TuTh 2:00 - 3:30PM — 210 South Hall
Instructor(s): Steven Weber

The introduction of technology increasingly delegates responsibility to technical actors, often reducing traditional forms of transparency and challenging traditional methods for accountability. This course explores the interaction between technical design and values including: privacy, accessibility, fairness, and freedom of expression. We will draw on literature from design, science and technology studies, computer science, law, and ethics, as well as primary sources in policy, standards and source code. We will investigate approaches to identifying the value implications of technical designs and use methods and tools for intentionally building in values at the outset.

TuTh 3:30 - 5:00PM — 202 South Hall
Instructor(s): Deirdre Mulligan

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.

TuTh 9:30 - 11:00AM — 210 South Hall
Instructor(s): Joshua Blumenstock
Laboratory Section 101
W 12:00-1:00pm — 210 South Hall
Instructor(s): Joshua Blumenstock

This course is a survey of Web technologies, ranging from the basic technologies underlying the Web (URI, HTTP, HTML) to more advanced technologies being used in the the context of Web engineering, for example structured data formats and Web programming frameworks. The goal of this course is to provide an overview of the technical issues surrounding the Web today, and to provide a solid and comprehensive perspective of the Web's constantly evolving landscape.

F 2:00-5:00PM — 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.

TuTh 3:30 - 5:00PM — 2060 Valley Life Sciences Building
Instructor(s): David Bamman

This course covers computational approaches to the task of modeling learning and improving outcomes in Intelligent Tutoring Systems (ITS) and Massive Open Online Courses (MOOCs). We will cover theories and methodologies underpinning current approaches to knowledge discovery and data mining in education and survey the latest developments in the broad field of human learning research. The course is project based; teams will be introduced to online learning platforms and their datasets with the objective of pairing data analysis with theory or implementation. Literature review will add context and grounding to projects.

TuTh 12:30 - 2:00PM — 2515 Tolman Hall
Instructor(s): Zachary Pardos
Students will receive no credit for C262 after taking 290 section 4. Three hours of lecture and one hour of laboratory per week. This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Also listed as New Media C262.
MW 10:30am - 12:00pm — 202 South Hall
Instructor(s): Kimiko Ryokai
Laboratory Section 101
W 3:00-4:00pm — 210 South Hall
Instructor(s): Kimiko Ryokai

Three hours of lecture per week. Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, causal inference, probability and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. No prerequisites, though an introductory course in statistics is recommended.

TuTh 12:30 - 2:00PM — 202 South Hall
Instructor(s): Coye Cheshire

Special Topics

Digital technologies have brought consumers many benefits, including new products and services, yet at the same time, these technologies offer affordances that alter the balance of power among companies and consumers. Technology makes it easier to deny consumers access to the courts; to restrict well-established customs and rights, such as fair use and the reselling of goods; to manipulate digital fora that provide reviews of products and services; to retaliate against and/or monitor or even extort consumers who criticize them; to engage in differential pricing; to “brick” or turn off devices remotely, to cause systemic insecurity by failing to patch products; and to impose transaction costs in order to shape consumer behavior.

Fundamentally, the move to digital turns many products into services. While the law has long comprehensively regulated products under the Uniform Commercial Code and products liability regimes, artifacts and services with embedded software present new challenges. European governments are moving aggressively to establish comprehensive regulations for digital goods. But no such agenda is on the horizon in the United States.

This course will employ a problem-based learning method (PBL). Students in the course will work in small groups to generate hypotheses, learning issues, and learning objectives in digital consumer protection. Through this process we will develop a high level conception of consumer protection and its goals. We will then explore its fit in the digital realm.

Students will develop short presentations on these learning objectives to create group learning and discussion. For the culmination of the course, students will work together to generate a research agenda for the future of digital consumer protection.

Section 1
M 2:00 - 5:00PM — 210 South Hall
Instructor(s): Chris Jay Hoofnagle

Information privacy law profoundly shapes how internet-enabled services may work. Privacy Law for Technologists will translate the regulatory demands flowing from the growing field of privacy and security law to those who are creating interesting and transformative internet-enabled services. The course will meet twice a week, with the first session focusing on the formal requirements of the law, and the second on how technology might accommodate regulatory demands and goals. Topics include:  Computer Fraud and Abuse Act (reverse engineering, scraping, computer attacks), unfair/deceptive trade practices, ECPA, children’s privacy, big data and discrimination (FCRA, ECOA), DMCA, intermediary liability issues, ediscovery and data retention, the anti-marketing laws, and technical requirements flowing from the EU-US Privacy Shield.

Required textbook: FTC Privacy Law and Policy (CUP 2016)

Section 2
MW 10:30AM - 12:00PM — 205 South Hall
Instructor(s): Chris Jay Hoofnagle

Much as Adam Smith saw his own age as marked by its engagement with “commerce” and thereby distinguished from all ages that had come before, it has become conventional to see our own era as a break from all that has preceded it, and thus distinguished principally by its engagement with information and computing technologies. Scholars have labeled the contemporary era as the “post-industrial,” “postmodern,” or “network society,” but probably the most widely used and enduring characterization distinguishes the present day as the “information age” or “information society.” This course will explore the notion of an “information society,” trying to understand what scholars have held to be the essential and distinguishing features of such a society, how these views compare with classic theories of society or with alternative accounts of the present age, and to what extent different conceptions of the “information age” are compatible. In pursuing this investigation, we shall bear in mind the admonition of the legal scholar James Boyle that while the idea of an “information age” may be “useful ... we need a critical social theory to understand it.” In the process of developing a critical, social, and political-economic analysis of this idea, we hope to assemble a corpus of information society readings.

Section 6
W 9:00AM - 12:00PM — 107 South Hall
Instructor(s): Ashwin Mathew, Paul Duguid

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 4
M 12:30 - 1:30PM — 205 South Hall

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.

Section 5
M 5:00 - 7:30PM (Sept 11 – Oct 16) — 205 South Hall
Instructor(s): Sara Cambridge

Science and Technology Studies (STS) is an interdisciplinary field concerned with two areas of interest to the I School: the interaction between digital technologies and the social; and the generation of knowledge. This class will be a seminar emphasizing close reading and discussion, primarily of classic STS works, along with more current research. We’ll be particularly interested in the interaction between STS and human-computer interaction (HCI), information and communication technologies for development (ICTD), and/or new media, but exactly what we read will depend on what interests we have in common.

Section 7
Tu 10:00am - 12:00pm — 107 South Hall
Instructor(s): Nancy Van House

Specific topics, hours and credit may vary from section to section, year to year. May be repeated for credit with change in content.

Section 3
Tu 2:00 - 3:00PM — 107 South Hall
Instructor(s): Joshua Blumenstock

This course is designed to give participants a practical overview of the modern lean/agile product management paradigm based on contemporary industry practice. We cover the complete lifecycle of product management, from discovering your customers and users through to sales, marketing and managing teams. We'll take an experimental approach throughout, showing how to minimize investment and output while maximizing the information we discover in order to support effective decision-making. During the course, we'll show how to apply the theory through hands-on collaborative problem-solving activities. There will also be guest lectures from industry experts.

This course satisfies the Management requirement for the MIMS degree.

In Fall 2015 & Fall 2016, this course was offered for 2 units.

Section 2
F 9:00AM - 12:00PM — 202 South Hall
Instructor(s): Jez Humble

Delivering value to enterprises and ensuring long-term career success requires much more than pure technology skills. This course is an industry technology executive’s view of how, as information becomes increasingly strategic for all organizations, future technology leaders can accelerate career growth and bring value to their organizations more quickly by developing this core set of business skills.

This course will explore a series of critical business topics that apply both to start-up and Fortune 500 enterprises. This course is divided into three primary sections, delivered through a series of readings, industry guest speakers and hands-on practice of business skills:

  • Examining business models and strategies: How do companies plan to succeed? What are their business strategies and how do those translate into technology strategies and investments in support of these plans? Secondly, how does one analyze whether an organization’s culture is enabling or inhibiting that success?

  • Interacting with SF Bay Area technology executives: Students will have access to C-level executives in an intimate classroom setting as they discuss their organizational strategies, cultures and technology styles. How do they trade off speed, quality and features? How do they manage innovation when they also must operate? Currently scheduled speakers for Fall 2017 include:

  • Enhancing core business skills: Presentation skills, negotiations, leadership styles, organizational change, personal brand and future career vision are topics that will be explored in class and in written assignments. A brief presentation will be required from all students.

Section 1
M 2:00 - 5:00PM — 205 South Hall
Instructor(s): Peter Weis

To what extent can a machine know the inner workings of a person's mind, even theoretically? This course explores this question through a mixture of hands-on machine learning and critical discussions on theory. In this course, students will practice ML techniques on a provided corpus of data to produce a working brain-computer interface. Simultaneously, students will engage critically with recent research in ubiquitous sensing technologies, and the discourse around them, tracing ideas to their origins in cognitive science.

This half-semester course runs for the first eight weeks of the semester (8/23/17 - 10/17/17).

Each week will cover one topic in mind-reading machines. Tuesday classes will be a lecture, a survey of the week's readings, centering around one or two particular papers. Thursday classes will be lab-time, centered around supporting assignments, projects and hands-on engagement with the course dataset.

This class is a pre-requisite for Info 290T. Projects on Mind-Reading Machines, an (optional) 1-unit course taking place in the second half of the semester, which would continue the themes of this course through a student-led research project.

Section 1
TuTh 2:00 - 3:30PM — 202 South Hall
Instructor(s): John Chuang, Nick Merrill

To what extent can a machine know the inner workings of a person's mind, even theoretically? This course explores this question through a mixture of hands-on machine learning and critical discussions on theory. In this course, students will practice ML techniques on a provided corpus of data to produce a working brain-computer interface. Simultaneously, students will engage critically with recent research in ubiquitous sensing technologies, and the discourse around them, tracing ideas to their origins in cognitive science.

This 1-unit course takes place in the second half of the semester, continuing the themes of Info 290T. Mind-Reading and Telepathy for Beginners and Intermediates through a student-led research project.

Section 2
Tu 2:00 - 3:30pm — 202 South Hall
Instructor(s): John Chuang, Nick Merrill

Seminar

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
W 2:00 - 4:00PM — 205 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.

M 12:30 - 1:30PM — 107 South Hall
Instructor(s): John Chuang, Kimiko Ryokai

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
F 3:00 - 5:00PM — 107 South Hall

Individual/Group Study

Course may be repeated for credit as topic varies. Four hours of work per week per unit. Must be taken on a satisfactory/unsatisfactory basis. Formerly Information 310. 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.
W 2:00-4:00 pm — 107 South Hall
Instructor(s): Paul Duguid