Topics include elements of the theory of matrices and vector spaces.

Studies some aspects of discrete mathematics. Topics include sets, functions, elementary probability, induction proofs, and recurrence relations.

Studies some aspects of discrete mathematics. Topics include sets, functions, elementary probability, induction proofs, and recurrence relations.

Studies some aspects of discrete mathematics. Topics include sets, functions, elementary probability, induction proofs, and recurrence relations.

Topics include the real number system, convergence of sequences and series, power series, uniform convergence, compactness and connectedness, continuity, abstract treatment of differential and integral calculus, metric spaces, and point-set topology.

Mathematical optimization involves finding the best solution to a problem from a set of feasible solutions defined by mathematical constraints. It has an elegant theory and applications in fields like management, economics, engineering, and computer science that require decision making under constraints on time or other resources. We will begin by studying linear optimization, including duality, the simplex algorithm, and the geometry of linear programming. Other topics will include discrete optimization, network optimization, and nonlinear optimization.

This course develops the ideas of probability simultaneously from experimental and theoretical perspectives. The laboratory provides a range of experiences that enhance and sharpen the theoretical approach and, moreover, allows us to observe regularities in complex phenomena and to conjecture theorems. Topics include: introductory experiments; axiomatic probability; random variables, expectation, and variance; discrete distributions; continuous distributions; stochastic processes; functions of random variables; estimation and hypothesis testing.

Introduces students to algorithms, basic data structures, and programming techniques. Students learn computing principles by exploring problems drawn from a broad set of domains, such as cryptography, data analysis and games.

Introduces students to algorithms, basic data structures, and programming techniques. Students learn computing principles by exploring problems drawn from a broad set of domains, such as cryptography, data analysis and games.

This course builds on the basic programming concepts learned in Computer Science 151, shifting the focus to the organization of data in order to improve efficiency and simplicity of programs. Topics include the study of abstract data types and data structures (such as linked lists, stacks, queues, and binary trees). This course is programming-intensive and introduces the Java programming language.