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Teaching Assistants as Tomorrow's Faculty prepares Teaching Assistants (TAs) at the College of Information and Computer Sciences to fulfill their duties in an effective and pedagogically sound manner. The one credit (not repeatable) course is semester long and taken by all TAs prior to assuming assistantship.

This course considers an array of ethical issues in computing. Readings, class discussions, and guest speakers will cover topics related to avenues of development in artificial intelligence, privacy, identity, inclusiveness, environmental responsibility, internet censorship, network policy, plagiarism, intellectual property and others. All examples will be drawn from current and recent events with readings from a range of sources both journalistic and academic.

This course surveys the principles of modern programming languages and provides a glimpse of contemporary programming languages research. This is an implementation-heavy course, where you will build typical language-based tools such as interpreters, type-checkers, and type inference algorithms. You will also implement emerging technologies, such as program verifiers and "fancy" type systems. This course satisfies a CS Elective for the CS Major (BS/BA).

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

Development of individual skills necessary for designing, implementing, testing and modifying larger programs: use of various modern automated tools, design strategies and patterns, working with large code bases and libraries, code refactoring.

This course is intended to cover information retrieval and other information processing activities, from an applied perspective. There will be numerous programming projects and quizzes. Topics will include: search engine construction (document acquisition, processing, indexing, and querying); learning to rank; information retrieval system performance evaluation; classification and clustering; other machine learning information processing tasks; and many more. This course counts as a CS Elective toward the BS/BA.