This course surveys the most important algorithms and data structures in use on computers today. Topics include elementary data structures, sorting algorithms, search algorithms and data structures, graphs, and some more advanced topics (randomization, multiplicative weights and intractability). The course concentrates on developing implementations, understanding their performance characteristics, and estimating their potential effectiveness in applications.

The course aims to help students think critically about the ethical and social aspects of computing technology. Topics include ethical foundations; political economy of the tech industry; algorithmic fairness; AI and labor; AI safety; AI and climate; social media and platform power; information security; privacy; values in design; research ethics; professional ethics; technology and social good; digital colonialism; law & policy. Activities will include readings, technical work, and case studies of contemporary debates.

An introduction to the principles of typed functional programming. Programming recursive functions over structured data types and informal reasoning by induction about the correctness of those functions. Functional algorithms and data structures. Principles of modular programming, type abstraction, representation invariants and representation independence. Parallel functional programming, algorithms and applications.