Introduction to Algorithms

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.

Lectures

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   Lecture 1 - Analysis of Algorithms

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   Lecture 2 - Asymptotic Notation and Recurrences

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   Lecture 3 - Divide and Conquer

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   Lecture 4 - Quicksort

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   Lecture 5 - Sorting Lower Bounds and Linear-Time Sorting

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   Lecture 6 - Order Statistics

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   Lecture 7 - Hashing I

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   Lecture 8 - Hashing II

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   Lecture 9 - Randomly Built Binary Search Trees

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    Lecture 10 - Balanced Search Trees

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    Lecture 11 - Augmenting Data Structures

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    Lecture 12 - Skip Lists

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    Lecture 13 - Amortized Analysis

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    Lecture 14 - Competitive Analysis

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    Lecture 15 - Dynamic Programming

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    Lecture 16 - Greedy Algorithms (and Graphs)

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    Lecture 17 - Shortest Paths I

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    Lecture 18 - Shortest Paths II

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    Lecture 19 - Shortest Paths III

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    Lecture 20 - Advanced Topics

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    Lecture 21 - Advanced Topics (cont.)

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    Lecture 22 - Advanced Topics (cont.)

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    Lecture 23 - Advanced Topics (cont.)