Fundamental Algorithms
Fall 2008
Professor Chee Yap

STATIC INFO:	Course Information \| One-time Action \| Lecture Notes and Textbook \| Course Description \|
DYNAMIC INFO:	NYU Bboard \| Homework Directory \| Lecture Directory \|
RESOURCES:	Past Course \| Useful Links \|

Course Information

Instructor	Chee Yap , Room: WWH 301, Tel: 998-3115, email: yap "at" cs.nyu.edu
Teaching Assistant	Marc Millstone, Room: WWH 530, Tel: (212)998-3293 email: millstone "at" cims.nyu.edu
Office Hours	Chee: Mon 4-5, Tue 5-6 and ~~Thu 4-5~~ Thu 3:30-4:30, and by appointment. Marc: Tue 2-3, and by appointment.
Lectures	Wed 5:00-6:50, WWH 109
Recitation	Mon 5:00-5:50, WWH 109
Homework	No late homework. With valid excuse, we have the option of imposing a penalty.
Grade	Course Grade is curved: Class Participation (5%) Written Homework (40%), Midterm (20%), Final (35%).
Exams	Midterm will be held in class on ~~March 5~~ March 10, starting at 4:45pm. T.A.'s extra office hrs (week of Mar 3): Tue 5-6, Wed 4-5, Thu 5:40-6:55. FINAL EXAM will be held in class, on the first class day (May 7) of exam period.
Computing	This course has no programming.
NYU Bboard	Class discussions, email lists, announcements, check grades, etc. Use your NYU NetID to login, then click our class link
Academic Integrity	Department statement -- please read this!

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Course Description

PREREQUISITE: An undergrad course in discrete mathematics. One year's experience in some high-level computer language and familiarity with recursive programming and basic data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
This course emphasizes analysis, correctness and efficiency of algorithms. As such, it is relatively mathematical and rigorous. Students are expected to work hard on this.
An important aspect of analysis is on solving recurrences. You will learn asymptotic notations (Big-Oh, etc).
Basic algorithms to know include balanced binary trees, sorting and selection, hashing, graph traversal, graph connectivity, breadth-first and depth-first searches, spanning tree, shortest paths.
Computational techniques include divide-and-conquer, greedy method, and dynamic programming.
Depending on time, we add some advanced topics such as: randomized algorithms, amortization, primality and cryptographic applications, and computational geometry.
The capstone of the course is a brief introduction to NP-Completeness Theory.

Click here for a rough outline of the course syllabus.

Lecture Notes and Textbook

I will use my own lecture notes, which are available for download from this website.
Although I do not require a textbook, many students find it helpful to read from a text book. I highly recommended the following book:
Introduction to Algorithms (2nd Edition) by Cormen, Leiserson, Rivest and Stein, 2002,
from the MIT Press and McGraw-Hill Book Co.
The viewpoint of this text book (known as [CLRS]) is consistent with my lecture notes. In fact, most of my lectures have a matching chapter in [CLRS]. It is a THICK book, but if you become somewhat familiar with it, it will serve nicely as your algorithms reference on your bookshelf!

Some Useful Links