Most of the data available on the web or managed by institutions and businesses consists of unstructured text. Natural language processing tools help to organize such texts, extract relevant information from them, and automatize time-consuming processes. This course will teach the fundamental knowledge necessary to design and develop end-to-end natural language understanding applications, drawn from examples such as question answering, sentiment analysis, information extraction, automated inference, machine translation, chatbots, etc. We will use several natural language processing toolkits and libraries.

This course provides a comprehensive survey of video game production practices. Students work on game development assignments for presentation in a professional portfolio. The course topics include: collaborative technologies, software design patterns for games, spatial transformations, and technical considerations surrounding game art, such as authoring sprites, 3D models, animations, texture mapping, and writing shaders. Students will be given periodic assignments that reinforce lessons from class.

Virtual reality is an emerging technology that has been widely used in recent years in various areas, such as education, training, well-being, and entertainment. Virtual reality offers a highly immersive experience as the head mounted displays replace the vision of the users with digital imagery. It encompasses many disciplines, such as computer science, human computer interaction, game design and development, information science, and psychology. This course merges a theoretical and practical approach to give students the necessary knowledge to design, develop, and critique virtual reality games and applications.

Machine learning describes the development of algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.

The field of Human-Computer Interaction (HCI) encompasses the design, implementation, and evaluation of interactive computing systems. This course will provide a survey of HCI theory and practice. The course will address the presentation of information and the design of interaction from a human-centered perspective, looking at relevant perceptive, cognitive, and social factors influencing in the design process. It will motivate practical design guidelines for information presentation through Gestalt theory and studies of consistency, memory, and interpretation. Technological concerns will be examined that include interaction styles, devices, constraints, affordances, and metaphors. Theories, principles and design guidelines will be surveyed for both classical and emerging interaction paradigms, with case studies from practical application scenarios. As a central theme, the course will promote the processes of usability engineering, introducing the concepts of participatory design, requirements analysis, rapid prototyping, iterative development, and user evaluation. Both quantitative and qualitative evaluation strategies will be discussed. This course is co-convened: Upper-level undergraduates and graduate students are encouraged to enroll. Graduate students will be expected to complete more substantial projects and will be given more in-depth reading assignments.

This course will introduce you to the burgeoning methodological subfield called computational social science: An evolving bundle of computationally sophisticated techniques that are increasingly used to answer research questions in the social sciences. The goal of the course is for you to learn how to produce your own computational social science research project aimed at a scholarly outlet. The course will survey the latest published research and train you in research design, methods, and techniques in computational social science. This might include topics such as Big Data, data visualization, social network analysis, agent-based modeling, and natural language processing, as well as identifying and developing variables, mechanisms, and research questions motivated by social science research. The course will also delve into the growing ethical challenges and considerations associated with computational social science research. The course is set up to welcome people from many different backgrounds, in particular those with or without prior exposure to programming, statistics, or the social sciences.

Organizational theory, structures, systems, and administrative procedures in varied higher education institutions; patterns of governance and policy development.

The development and exchange of scholarly information, usually in a small group setting. The scope of work shall consist of research by course registrants, with the exchange of the results of such research through discussion, reports, and/or papers.

The development and exchange of scholarly information, usually in a small group setting. The scope of work shall consist of research by course registrants, with the exchange of the results of such research through discussion, reports, and/or papers.

The practical application, on an individual basis, of previously studied theory and the collection of data for future theoretical interpretation.