The aim of this course is to provide students interested in computer science an introduction to vectors and matrices and their use in modeling and data analysis. The course will be driven by applications from areas chosen from among: combinatorial optimization, computer vision, cryptography, game theory, graphics, information retrieval and web search, maching learning, and scientific visualization. For example, students will learn Google's PageRank method for ranking web pages. This course satisfies the linear algebra requirement for the Computer Science Sc.B.

There are no formal prerequisites, but students are expected to be comfortable with mathematics. First-year students are welcome.

Instructor: Phil Klein
Head TA: Sarah Meiklejohn
UTAs: Dan Butler, Matt Mallozzi, Matthew Schiffman

Meeting time: J Hour (T.,Th. 1:00-2:20 PM) at Watson (CIT) Center 367

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