MIT : Introduction to Algorithms (2009)

Tutorial & e-learning. May 1, 2011 by sputnik.


MIT : Introduction to Algorithms (2009)
23 Lectures | 3.68 GB
Genre: eLearning

Instructors: Prof. Erik Demaine & Prof. Charles Leiserson

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.

Video Lectures

01 _ Administrivia/Introduction/Analysis of Algorithms, Insertion Sort, Mergesort
02 _ Asymptotic Notation/Recurrences/Substitution, Master Method
03 _ Divide-and-Conquer: Strassen, Fibonacci, Polynomial Multiplication
04 _ Quicksort, Randomized Algorithms
05 _ Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort
06 _ Order Statistics, Median
07 _ Hashing, Hash Functions
08 _ Universal Hashing, Perfect Hashing
09 _ Relation of BSTs to Quicksort/Analysis of Random BST
10 _ Red-black Trees, Rotations, Insertions, Deletions
11 _ Augmenting Data Structures, Dynamic Order Statistics, Interval Trees
12 _ Skip Lists
13 _ Amortized Algorithms, Table Doubling, Potential Method
14 _ Competitive Analysis: Self-organizing Lists
15 _ Dynamic Programming, Longest Common Subsequence
16 _ Greedy Algorithms, Minimum Spanning Trees
17 _ Shortest Paths I: Properties, Dijkstra's Algorithm, Breadth-first Search
18 _ Shortest Paths II: Bellman-Ford, Linear Programming, Difference Constraints
19 _ Shortest Paths III: All-pairs Shortest Paths, Matrix Multiplication, Floyd-Warshall, Johnson
20 _ Advanced Topics
21 _ Advanced Topics (cont.)
22 _ Advanced Topics (cont.)
23 _ Advanced Topics (cont.)

Running time for each video about 1:10:00 to 1:25:00

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