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There is no mandatory textbook for the course. We will provide lecture notes, or reading from books. Some good books include: The Design and Analysis of Algorithms by Dexter Kozen: CMU Access via SpringerLink. Algorithm Design by Kleinberg and Tardos: CMU library, slides by Kevin Wayne. Algorithms by Dasgupta, Papadimitriou, and Vazirani (DPV): CMU library, author's site. Introduction to Algorithms by Cormen, Leiserson, Rivest, and Stein (CLRS): CMU library with links to e-copy. Randomized Algorithms by Motwani and Raghavan: CMU Access to e-copy. Algorithms by Jeff Erickson: online PDF. Design and Analysis of Computer Algorithms by Aho, Hopcroft, and Ullman: CMU library. We assume basic discrete mathematics (counting, basic probability, basic graphs theory, basic linear algebra): some resources include: (15-251) Great Theoretical Ideas in CS: slides from our undergraduate course. (15-151) Discrete Mathematics: our undergraduate course page (contains the textbook). Mathematics for Computer Science by Lehman, Leighton, and Meyer: lecture notes from MIT.A useful stackexchange thread on good linear algebra sources (with links to several free texts).A primer on matrices (by Shephen Boyd), and a linear algebra review (from Stanford's cs229, by our own Zico Kolter) with some multivariate calculus too. Some helpful videos on linear algebra (thanks Anil!). 1e1e36bf2d