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.

The primary objective of this course is the development of language teachers' assessment literacy, which includes knowledge of key assessment principles and skill in creating or adopting assessment tools and procedures for the language classroom. Participants in this course will develop their knowledge and skills related to assessing all skill areas in the language classroom, including productive skills (writing, speaking), receptive skills (reading, listening), and assessing grammar and vocabulary. Grading and student evaluation will also be important topics of consideration and exploration in this course. Designed specifically for in-service (and pre-service) language teachers, the course combines theory with practice by covering essential principles of effective classroom assessment and the development of effective assessment tools for classroom use. Participants completing this course will become more assessment literate and better able to evaluate student performance in their classrooms fairly and effectively.

Issues in and methods of applied linguistics with emphasis on Germanic languages. Student taking GER 480 for German Studies credit must have advanced proficiency in German. Graduate-level requirements include an in-depth research paper on an aspect of applied linguistic research. Students taking GER 580 for German Studies credit must have advanced proficiency in German.

This class is intended to prepare students to teach a second/foreign language. It will provide students with the essential foundations in language teaching methodology and theory, pedagogical grammar, curriculum and materials development, classroom management, and formal and informal assessment techniques. In addition, students will gain practical knowledge through video practicum components, which allow them to watch and reflect on actual classroom teaching. Students will complete this program with the pedagogical knowledge and practical understanding necessary to be confident and effective language teachers.