Students are encouraged to create a course sequence that best supports their individual research, dissertation, and teaching plans. See the TSB Handbook for more information on course requirements. Recently offered courses include:
Theories & Practice of Human Computer Interaction
Instructor: Darren Gergle
This graduate seminar course provides a broad and comprehensive introduction to human- computer interaction theory and research. The primary objective of the course is to equip students with an understanding of seminal work in the area of Human-Computer Interaction (HCI) through a combination of readings, videos, interactive demos and discussion. The course begins with a history of both applied and theoretical work in the area of human-computer interaction, and then transitions to cover the rapidly changing technological landscape and explore current and future research areas. Topics will include interaction techniques, social computing and the social web, context-aware computing, mobile interfaces, information visualization, among other topics. The course is run as a seminar and students will be responsible for weekly readings, leading weekly discussions, written assignments, and a final exam.
Social Network Analysis
Instructor: Noshir Contractor
Networks play an increasingly important role in our understanding of human behavior. In communication and the organizational sciences, extraordinary developments in computing and telecommunications have engendered new organizational forms based on fluid, dynamic networks. These new network forms of self-organization are constantly evolving in dynamic communities as new network links are created and dysfunctional ones dissolved. While many writers assert that the capability to nurture networks will differentiate dominant 21st century organizations, little is known about how this important organizational form emerges and evolves.
This seminar is intended to review theoretical, conceptual, and analytic issues associated with network perspectives on communicating and organizing. The course will review scholarship on the science of networks in communication, computer science, economics, engineering, organizational science, life sciences, physical sciences, political science, psychology, and sociology in order to take an in-depth look at theories, methods, and tools to examine the structure and dynamics of networks.
Most class time will be spent discussing the assigned readings. Laboratory exercises will provide experience with network analysis, modeling and visualization tools. A term paper is expected advancing some theoretical, methodological, or computational aspect of network science.
Algorithmic News Media
Instructor: Nick Diakopoulos
The increasing role that algorithms and automation are playing in the production of information is rapidly changing the ways in which the news media is authored, curated, disseminated, and consumed. This graduate seminar provides an overview of the latest developments in algorithmic news media on topics including journalistic data mining, automated content production, news bots, platform dissemination, and algorithmic accountability and transparency. Themes of value-sensitive design, labor, and sustainability will be discussed with respect to how algorithms impact the public sphere.
Instructor: Nick Diakopoulos
This research seminar offers an overview of the emerging field of computational communication including a sampling of methods for computational modeling, content analysis, network analysis, and simulation of communication-oriented data, as well as the application of critical approaches, and the empirical study of computational communication systems mediated by algorithms and AI. Through engagement with the latest scholarship in the field, the emphasis of the course is not on methodological mechanics, but rather on developing conceptual, strategic, and critical thinking to assess opportunities that computational methods and computational advances in society create for communication research.
Human-Centered Machine Learning
Instructor: Stevie Chancellor
Machine learning has stirred tremendous excitement in popular science, as it is being applied to problems of all shapes and sizes. This involves learning and predicting things that humans do naturally (see, hear, understand language), signals we give off unconsciously, as well as decisions and ideas we have (make decisions, evaluate options, think creatively). These are captured under the broad banner of human-centered machine learning (HCML), an emergent research and practical area combining human interaction with machine learning and studying the impacts of systems in the world.
In this class, we will study what human-centered machine learning is, how this idea is built and applied in technical systems, and critiques of machine learning from other domains that question if ML can be human-centered. There will be a strong emphasis in this seminar on reading and discussing current research on HCML, from a technical, critical, and social perspectives. Later, we will put these principles into practice in applying what we have learned in a group project. This class is intended to be interdisciplinary, with students drawn from CS and STEM as well as social sciences who use ML for their research and are thinking through essential questions on the impacts of ML in society.
Qualitative Methods for Technology Design
This course is for graduate students who will conduct qualitative research on the behavioral and social aspects of technology use and technology design. The objective of this course is to give MTS and TSB students practical, hands-on experience with observation, interviewing, and qualitative analysis techniques. The course is run as a working seminar. Students are expected to collect their own qualitative data throughout the term. Students are also responsible for selecting and leading discussion on weekly readings. Assignments throughout the term will allow students to apply the methods discussed in class. In addition to four assignments, students will submit a final course paper in which they are expected to apply the methods learned about in the course.
The Design of Technological Tools for Thinking and Learning
Instructor: Uri Wilensky
This course is a hands-on practicum in designing and building technology-enabled curricula and learning environments. We will use many rich software toolkits designed to enable novice computer users to get their “hands dirty” doing iterative software design. In addition to the hands-on component, the course is also designed to introduce you to the Constructionist Learning design perspective – first named by Seymour Papert and greatly influenced by the work of Jean Piaget – a perspective that is very influential in the Learning Sciences today.
Instructor: Bryan Pardo
Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. Some representations make it easier to learn tasks (e.g., face recognition or spoken word recognition) from examples. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.
In this course students will study deep learning architectures such as autoencoders, , convolutional deep neural networks, and recurrent neural networks. They will read original research papers that describe the algorithms and how they have been applied to fields like computer vision, automatic speech recognition, and audio event recognition.
For projects, students can work on their own or in groups (recommended) to write a codebase that reproduces a landmark research paper. This course is aimed at advanced undergraduates, masters, and PhD students.
Complex Network Analysis
Instructor: Emoke-Agnes Horvat
Ubiquitous networks have transformed fundamentally the way we experience and interact with the world. Network science aims to capture the essence of this interconnectedness. This graduate seminar is centered on reading articles that represent cutting edge research on social, communication, and economic networks. It covers the following topics: the role of connectedness in success, superstars and influentials, recommendation, crowd wisdom and herding, teams and collaboration, machine learning, and experimentation on networks. Students learn about a selection of techniques for exploratory visualization, statistical modeling, stochastic simulation, text mining, and predictive analytics. The studied methods are useful across a variety of fields in which aggregate human behavior and network effects play a crucial role.
Foundational Theories of Media and Communication
Instructor: Thomas J Billard
Communication” as a field does not, per se, exist. As Silvio Waisbord has argued, communication is a “post-discipline,” held together more so by a set of academic institutions, professional societies, and publication venues than by any coherent canon of theories or set of standard methodologies. Yet you are here to receive training in this so-called field, under the presumption that you will become experts in “communication.” This graduate seminar provides an overview of theories that are widely considered foundational to various subdisciplines under the umbrella of communication studies. The goal here is not to exhaust the set of “core” theories, but to provide a starting point for investigation into a few of the most common areas of study. Additionally, the seminar aims to train students in the process of producing social theory regarding media and communication.
Communicating Your Research
You have an exciting research finding. But to inform and inspire your audience, you need to communicate it in a way that is clear, relatable, engaging, and memorable. Most graduate students spend surprisingly little time learning this critical professional skill. This studio course will introduce you to the fundamentals of communication design, including graphic design, cognitive resonance, data visualization, and storytelling. You will also learn the design process, which emphasizes collaboration and rapid iteration – expressing and testing ideas with an audience in a virtuous cycle. You will develop this understanding through weekly presentations, studio critique, and in-class and out of class assignments. No previous experience is required. This interdisciplinary graduate seminar is targeted to Ph.D. students across domains, with the additional benefit of broadening your intellectual and social networks across programs at Northwestern.
Statistics and Statistical Programming
Instructors: Aaron Shaw
This course provides a get-your-hands-dirty graduate-level introduction to applied statistics and statistical programming focused on the empirical social sciences and social computing research. Course participants will acquire the conceptual, technical, and practical skills to conduct independent statistical analyses and become more sophisticated consumers of quantitative research in communication, HCI, and adjacent disciplines. The course will probably cover techniques such as: hypothesis testing, t-tests, chi-squared tests, ANOVA, linear regression; and logistic regression using the R programming language for all examples and assignments. We will also consider salient issues in quantitative empirical research such as reproducibility and the statistical crisis in science. We may cover other topics as time and interest allow.