Information Course Schedule Fall 2018

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. 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.

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.

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

The ability to manipulate, explore, and analyze structured data sets is foundational to the modern practice of data science.  This course introduces students to data analysis using the Python programming language, especially the core packages NumPy and pandas.  Student learn to operate on data, think critically about features they uncover, and organize results into a persuasive analysis.  Best practices for writing code in a functional style are emphasized throughout the course.   A set of weekly programming assignments reinforces and builds upon the techniques presented in lecture.  The course culminates in a final project in which students write a professional quality analysis based on their own research questions.

This course forms the second half of a sequence that begins with INFO 206.  It may also be taken as a stand-alone course by any student that has extensive Python experience.

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.

This course is a graduate-level introduction to HCI research. Students will learn to conduct original HCI research by reading and discussing research papers while collaborating on a semester-long research project. The class will focus on both the positive potentials of technology as well as the negative consequences that new technologies may have on society. Each week the class will focus on a theme of HCI research and review foundational, cutting-edge, and critical theory research relevant to that theme.

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.

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.

This course will explore how legal, ethical, and economic frameworks enable and constrain security technologies and policies. As digital technologies penetrate deeply into almost every aspect of human experience, a broad range of social-political-economic-legal-ethical-military and other non-technical considerations have come to envelope the cybersecurity landscape. Though cybersecurity itself is a technical discipline, these non-technical considerations constrain it, enable it, and give it shape. We will explore the most important of these elements. The course will introduce some of the most important macro-elements (such as national security considerations and the interests of nation-states) and micro-elements (such as behavioral economic insights into how people understand and interact with security features). Specific topics include policymaking (on the national, international, and organizational level), business models, legal frameworks (including duties of security, privacy issues, law enforcement access issues, computer hacking, intellectual property, and economic/military/intellectual property espionage), standards making, and the roles of users, government, and industry.

For individuals and organizations involved in political advocacy, cybersecurity threats are an increasingly common reality of operating in the digital world. Civil society has always been under attack from ideological, political, and governmental opponents who seek to silence dissenting opinions, but the widespread adoption of connected technologies by the individuals and organizations that make up civil society creates a new class of vulnerabilities.

Citizen Clinic at the Center for Long-Term Cybersecurity provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. Clinic students will participate in both a classroom and clinic component. In the classroom, students will study the basic theories and practices of digital security, the intricacies of protecting largely under-resourced organizations, and the tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic component, students will work in teams supervised by the Clinic staff to provide direct cybersecurity assistance to civil society organizations. Students’ clinic responsibilities will include learning about an organization’s mission and context, assessing its vulnerabilities, and ultimately recommending and implementing mitigations to the identified security risks. The emphasis will be on pragmatic, workable solutions that take into account the unique operational needs of each client organization.

Weekly lectures will provide students with the background information and tools they will need to engage with clients. Coursework will focus on client-facing, hands-on projects. Students will be expected to work an average of 12 hours per week, although the distribution of this workload may fluctuate based upon the availability and needs of the client. Enrollment will be by application and limited to graduate students and exceptional upper-level undergraduate students. While some background in cybersecurity will be useful for the course, it is not required. Given the interdisciplinary nature of this field, demonstrated ability in technology, law, policy, foreign language, or other applicable skills will also be desirable. All interested students will be placed on the waitlist and then contacted with instructions to apply for admission to the course. Students should be prepared to submit a résumé and a brief explanation of their interest in the Citizen Clinic, as well as a description of any applicable background experience.

Contact Steve Trush with any questions.

This course gives participants hands-on software product design experience based on current industry practice. The course is project-based with an emphasis on iteration, practice, and critique from experienced industry designers. During the course, participants work iteratively on a series of design projects (both solo and in groups) through a full design process, including developing appropriate design deliverables and gathering feedback. We’ll also cover specific topics, including design and prototyping tools, working with and developing design systems, typical phases and deliverables of the design process, and designing in different contexts (e.g. startups vs. larger companies). There will also be guest lectures from industry experts.

This course introduces students to experimentation in the social sciences. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology have facilitated the development of better data gathering. Key to this area of inquiry is the insight that correlation does not necessarily imply causality. In this course, we learn how to use experiments to establish causal effects and how to be appropriately skeptical of findings from observational data.

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 include:

  • Dick Daniels — CIO of Kaiser Permanente
  • Michael Kelly — CIO of Red Hat
  • Hugo Evans — VP of Data Science at A.T. Kearney
  • Roy Bahat — Head of Bloomberg Beta Venture Capital
  • Diana McKenzie — Former Workday CIO, current advisor and board member.

• 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.

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.

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.

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.