Introduction to Algorithms (SMA 5. Electrical Engineering and Computer Science. Course Features. Course Highlights. This course features a complete set of lecture notes and videos.
The course textbook was co- written by Prof. Leiserson. Course Description. This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide- and- conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number- theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing. This course was also taught as part of the Singapore- MIT Alliance (SMA) programme as course number SMA 5. Analysis and Design of Algorithms).
I just finished watching the last lecture of MIT's 'Introduction to Algorithms' course. Having a great passion for all aspects of computing, I decided to share everything I learned with you, my dear readers! As you all may know, I watched and posted my lecture notes of the whole MIT Introduction to Algorithms course. In this post I want to summarize all the topics that were covered in the lectures and point out some of the most interesting things in them.
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data str.
Lecture 18 – Dynamic Programming I: Fibonacci, Crazy Eights, sequence alignment () notes | substring matching | no recitation | readings: 15.3-15.4. Cover of 6.046J textbook, Introduction to Algorithms, Second Edition, by Cormen, Leiserson, Rivest, and Stein. (Image courtesy of MIT Press.). Want to watch this again later? Sign in to add this video to a playlist. Lecture 01: Administrivia; Introduction; Analysis of Algorithms, Insertion Sort, Mergesort View the complete course at: http:// License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu. Buy Introduction to Algorithms, 3rd Edition (MIT Press) on Amazon.com FREE SHIPPING on qualified orders.
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.
Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow; computational geometry; number-theoretic algorithms; polynomial and matrix calculations; caching; and parallel computing. This course was also taught as part of the Singapore-MIT Alliance (SMA.