Audience:- CS 573 is a broad introduction to algorithms aimed at graduate students in computer science and related areas, including students in the MCS and 5-year BS/MS programs. Mathematically/algorithmically mature undergraduates and graduate students in other departments are also welcome to register, but please come talk to me first. Most undergraduates should take CS 473 instead.

Prerequisites:- Students are assumed to have
masteredthe material taught in CS 173 (discrete mathematics) and CS 225 (basic algorithms and data structures). We emphasize that "mastery" is not the same as "exposure" or even "a good grade"; hence, Homework Zero. Programming experience is helpful; a strong mathematics background is even more helpful. We also assume a level of mathematical, algorithmic, and general intellectual maturity consistent with admission to a top-5 graduate program in computer science.

Coursework:- Course grades are based on homeworks (30% total), two midterms (20% each), and a final exam (30%). There will be several opportunities for extra credit, which will be applied
afterthe curve. All homework and exam grades will be posted on Compass Gradebook ('My Grades' under 'My Tools'). See the grading policies for more details.

Reading material:There is no required textbook.Lecture notes will be posted to the course web site as the semester progresses. Jeff's lecture notes, homeworks, and exams from previous semesters are already available. Algorithms lecture notes, slides, homeworks, and exams on the web from other UIUC faculty and from several other universities can be found on the web:

- Sariel Har-Peled (CS 473G = CS 573, Fall 2003 and Fall 2007)
- Chandra Chekuri (CS 473, Fall 2009)
- Mahesh Viswanathan (CS 473, Spring 2008).
- MIT (Spring 2008; Fall 2005, with video and alternate notes)
- CMU (Fall 2008; Spring 2010)
- University of Washington (several semesters)
For students who prefer an actual dead-tree reference, we recommend the following textbooks. The campus bookstore probably doesn't have them, but they're cheaper online anyway. Grainger Library has copies of all these books on reserve.

- Algorithm Design by Jon Kleinberg and Éva Tardos (Addison-Wesley, 2005). Based on algorithms classes at Cornell.
- Algorithms by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani (McGraw-Hill, 2006). Based on the undergraduate algorithms course at Berkeley; a complete PDF draft of the book is available online.
- Introduction to Algorithms by Thomas H. Cormen, Charles Leiserson, Ronald L. Rivest, and Clifford Stein (MIT Press/
McGraw-Hill, 2009). Based on algorithms classes at MIT. The first and second editions are also fine. Finally, as a review of important mathematical prerequisites, we recommend Eric Lehman and Tom Leighton's extensive lecture notes for the Mathematics for Computer Science course at MIT.

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