Students enrolled in this course attend weekly pedagogy sessions led by the instructors and facilitate a weekly PLTL session (1 hour) for a pre-assigned group of 8 to 10 students from CHEM 111 or CHEM 112. This course trains students to become effective leaders for Peer-Led Team Learning (PLTL) groups in the large enrollment general chemistry courses. The weekly pedagogy sessions will equip students with collaborative learning strategies, group facilitation techniques, leadership skills, and communication skills.

Students enrolled in this course attend weekly pedagogy sessions led by the instructors and facilitate a weekly PLTL session (1 hour) for a pre-assigned group of 8 to 10 students from CHEM 111 or CHEM 112. This course trains students to become effective leaders for Peer-Led Team Learning (PLTL) groups in the large enrollment general chemistry courses. The weekly pedagogy sessions will equip students with collaborative learning strategies, group facilitation techniques, leadership skills, and communication skills.

The course introduces advanced central topics in biostatistics and health data science including survival analysis, design and analysis of clinical trials, models for correlated data, bayesian modeling, and causal inference. The course motivates statistical reasoning and methods through substantive research questions and features of data typically available in public health and biomedical research. Students will obtain hands-on experience in applying selected methods on real data using the statistical programming language R.

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This course introduces methods for analyzing and drawing reliable conclusions from economic data. The goal is to develop an intuitive understanding of econometric techniques used in answering research questions in the social sciences, management, and public policy. The course emphasizes computation rather than formal derivations. Students will practice using statistical software to conduct data analysis, visualization, and report writing.

This course introduces methods for analyzing and drawing reliable conclusions from economic data. The goal is to develop an intuitive understanding of econometric techniques used in answering research questions in the social sciences, management, and public policy. The course emphasizes computation rather than formal derivations. Students will practice using statistical software to conduct data analysis, visualization, and report writing.

This course introduces methods for analyzing and drawing reliable conclusions from economic data. The goal is to develop an intuitive understanding of econometric techniques used in answering research questions in the social sciences, management, and public policy. The course emphasizes computation rather than formal derivations. Students will practice using statistical software to conduct data analysis, visualization, and report writing.

Artificial Intelligence will radically change education. Through machine learning, data mining, analytics, robotics, and user models, AI will replace false learning boundaries (e.g., learning places, time, level of study); personalize learning; make learning instantly available to everyone; connect learners with partners; provide multi-media; and augment human learning ability. This seminar examines recent work in explanatory and tutoring systems, presents theories about digital teaching and learning, and describes how to deliver personalized teaching in online systems.

This course will provide an introduction to fundamental principles underlying quantum communication, including quantum repeaters, entanglement, and other tools needed for the implementation of distributed quantum protocols. Topics include: basics of quantum computation and information; modeling and coping with noise in quantum systems; entanglement generation schemes; quantum Internet and its applications. Meets with and is similar to COMPSCI 690QC, with the exception that the final project requires students to read a research paper and compose a detailed summary of it.