Introduces students to the principal activities and state-of-the-art techniques involved in developing high-quality software systems. Topics include: requirements engineering, formal specification methods, design principles & patterns, verification & validation, debugging, and automated software engineering.

Introduces students to the principal activities and state-of-the-art techniques involved in developing high-quality software systems. Topics include: requirements engineering, formal specification methods, design principles & patterns, verification & validation, debugging, and automated software engineering.

Mathematical and algorithmic foundations of big data processing. Study of methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning.

Mathematical and algorithmic foundations of big data processing. Study of methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning.

Introduction to mathematical logic: propositional logic, first-order logic, completeness, emphasizing applications to computer science including SAT solvers, model checking, and resolution theorem proving.

Introduction to mathematical logic: propositional logic, first-order logic, completeness, emphasizing applications to computer science including SAT solvers, model checking, and resolution theorem proving.

Informed by critical, cultural, social and technological theories, texts and ideas covered in this course target the intersections of computing and ethical reasoning. Students will be presented with questions to ask, possible theories to draw from, and best practices to consult in considering the ethical impacts of computing. Course materials reflect diversity in authorship, perspective, and epistemology. This course counts as an Elective for the CS and INFORM Majors.

Informed by critical, cultural, social and technological theories, texts and ideas covered in this course target the intersections of computing and ethical reasoning. Students will be presented with questions to ask, possible theories to draw from, and best practices to consult in considering the ethical impacts of computing. Course materials reflect diversity in authorship, perspective, and epistemology. This course counts as an Elective for the CS and INFORM Majors.

Introduction to formal language theory. Topics include finite state languages, context-free languages, the relationship between language classes and formal machine models, the Turing Machine model of computation, theories of computability, resource-bounded models, and NP-completeness. It is recommended that students have a B- or better in COMPSCI 311 in order to attempt COMPSCI 501.

Introduction to formal language theory. Topics include finite state languages, context-free languages, the relationship between language classes and formal machine models, the Turing Machine model of computation, theories of computability, resource-bounded models, and NP-completeness. It is recommended that students have a B- or better in COMPSCI 311 in order to attempt COMPSCI 501.