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!).
Material Science Book By Raghavan Pdf Free 750
Download: https://shurll.com/2vKyBT
2ff7e9595c
Comments