Information Course Schedule Fall 2016

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.

This course provides an introduction to critical and ethical issues surrounding data and society. It blends social and historical perspectives on data with ethics, policy, and case examples to help students develop a workable understanding of current ethical issues in data science. Ethical and policy-related concepts addressed include: research ethics; privacy and surveillance; data and discrimination; and the “black box” of algorithms. Importantly, these issues will be addressed throughout the lifecycle of data — from collection to storage to analysis and application. Course assignments will emphasize researcher and practitioner reflexivity, allowing students to explore their own social and ethical commitments.

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. Also listed as Sociology C167.

Course may be repeated for credit. Three hours of lecture per week for five weeks.

From criminal justice to health care to municipal services, civic technology is transforming the public sector. Taught by experts from the California Department of Justice, this course explores the emerging disciplines of data science, digital services, and user­-centered design and their implications for government and public policy.

This is a weekly one-hour seminar on the latest topics in the field of Natural Language Processing (also known as Computational Linguistics). Researchers from across UC Berkeley as well as visitors from out of town will present their recent work for discussion and feedback. Past topics have included multilingual language processing, analyzing social text, analyzing text using joint models, unsupervised morphology induction using word embeddings, deep learning of visual question answering, and unsupervised transcription of music and language.

In Fall 2016, we will meet every week, with alternating weeks consisting of discussions of readings and presentations of new research by local and visiting speakers.

Anyone is welcome to audit the course. Graduate students and undergraduates may enroll in this course for 1 unit of credit. In order to earn that unit of credit, students must write a synopsis of a research paper every two weeks, must attend at least 11 class meetings (and arrive on time), and must lead (or co-lead) at least one discussion of a research paper during the course of the semester.

15 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.

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.

This course will provide an introduction to the field of human-computer interaction (HCI). Students will learn to apply design thinking to user experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI.

Three hours of lecture per week. This course covers the practical and theoretical issues associated with computer-mediated communication (CMC) systems (e.g., email, newsgroups, wikis, online games, etc.). We will focus on the analysis of CMC practices, the relationship between technology and behavior, and the design and implementation issues associated with constructing CMC systems. This course primarily takes a social scientific approach (including research from social psychology, economics, sociology, and communication).

This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, we start with the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams.

"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.

This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core front-end languages and frameworks (HTML/CSS/JavaScript/React/Redux), as well as the underlying technologies that enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the web’s constantly evolving landscape.

Three hours of lecture per week. Letter grade to fulfill degree requirements. Prerequisites: Proficient programming in Python (programs of at least 200 lines of code), proficient with basic statistics and probabilities. This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.

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.

Three hours of lecture per week. Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research.

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.

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.

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

What insights about student learning can be revealed from data, and how can those insights be used to improve the efficacy of educational technology? This course will cover 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.

This course will be project based, where 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 serve to add context and grounding to projects.

Suggested background includes one programming course and familiarity with one statistical/computational software package.

The study of learning in online environments is an interdisciplinary pursuit, and therefore all majors are welcomed and encouraged to bring complimentary backgrounds.

Undergraduates with the appropriate background and motivation are encouraged to enroll but must contact Associate Director of Student Affairs Catherine Cronquist Browning for enrollment permissions.
 

This is a weekly one-hour seminar on the latest topics in the field of Natural Language Processing (also known as Computational Linguistics). Researchers from across UC Berkeley as well as visitors from out of town will present their recent work for discussion and feedback. Past topics have included multilingual language processing, analyzing social text, analyzing text using joint models, unsupervised morphology induction using word embeddings, deep learning of visual question answering, and unsupervised transcription of music and language.

In Fall 2016, we will meet every week, with alternating weeks consisting of discussions of readings and presentations of new research by local and visiting speakers.

Anyone is welcome to audit the course. Graduate students and undergraduates may enroll in this course for 1 unit of credit. In order to earn that unit of credit, students must write a synopsis of a research paper every two weeks, must attend at least 11 class meetings (and arrive on time), and must lead (or co-lead) at least one discussion of a research paper during the course of the semester.

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)

Course may be repeated for credit. Three hours of lecture per week for five weeks.

Every business depends on information — about customers, competitors, trends, performance, etc. Entire curricula have been focused on the technological, systems, strategic, and management challenges associated with that dependency. This course, however, looks at a different intersection between information and business. Specifically, it will explore how entrepreneurs across the world are developing ventures fundamentally centered on new and emerging information technologies and the business models and strategies they make possible. These include not only the Googles, Amazons, and Facebooks of the world, but also ventures like Comat and Samasource. In some cases, these are efforts on the proverbial cutting edge of technology; more often they involve creative application and/or integration of existing information technologies in innovative ways.

We will first examine the key elements of business models and the entrepreneurial process, before looking in more detail at a variety of ventures leveraging information-based technologies and strategies in an array of markets. Using of mix of case-study discussion, short lectures, and focused conversations with active entrepreneurs, this will be a highly interactive and collaborative course — not a sit-listen-take-notes type of class.

Expect to be actively involved in a series of in-class and outside assignments, both individual- and team-based, that will help you develop an understanding of how entrepreneurs are using information-centric technologies to create new markets and redefine old ones, and the lessons learned along the way. You may also explore your own ideas for new ventures along the way.

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.

Students will build tools to explore and apply theories of information organization and retrieval. Students will implement various concepts covered in the concurrent 202 course through small projects on topics like controlled vocabularies, the semantic web, and corpus analysis. We will also experiment with topics suggested by students during the course. Students will develop skills in rapid prototyping of web-based projects using Python, XML, and jQuery.

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.

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.

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.

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.