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Lecture 1 : Insertion sort

Lecture 2 : Analysis of Insertion Sort

Lecture 3 : Asymptotic Analysis

Lecture 4 : Recurrence of Merge Sort

Lecture 5 : Substitution Method

Lecture 6 : The Master Method

Lecture 7 : Divide-and-Conquer

Lecture 8 : Divide-and-Conquer (Contd.)

Lecture 9 : Straseen's Algorithms

Lecture 10 : QuickSort

Lecture 11 : Analysis of Quicksort.

Lecture 12 : Randomized Quicksort

Lecture 13 : Heap

Lecture 14 : Heap Sort

Lecture 15 : Decision Tree

Lecture 16 : Linear time Sorting

Lecture 17 : Radix Sort & Bucket Sort

Lecture 18 : Order Statistics

Lecture 19 : Randomised Order Statistics

Lecture 20 : Worst case linear time order statistics

Lecture 21 : Hash Function

Lecture 22 : Open Addressing

Lecture 23 : Universal Hashing

Lecture 24 : Perfect Hashing

Lecture 25 : Binary Search Tree (BST) Sort

Lecture 26 : Randomly build BST

Lecture 27 : Red Black Tree

Lecture 28 : Red Black Tree (Contd.)

Lecture 29 : Augmentation of data structure

Lecture 30 : Interval trees

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Lecture 36 : Dynamic Programming

Lecture 37 : Longest common subsequence

Lecture 38 : Graphs

Lecture 39 : Prim's Algorithms

Lecture 40 : Graph Search

Lecture 41

Lecture 42

Lecture 43

Lecture 44

Lecture 45

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Description:

PRE-REQUISITES: Nil INTENDED AUDIENCE: UG, PG, B. Tech., M. Tech., M. Sc. INDUSTRIES APPLICABLE TO: IT Companies COURSE OUTLINE: 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.

Introduction to Algorithms and Analysis

NPTEL

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#Computer Science #Algorithms and Data Structures #Insertion Sort #Algorithms #Data Structures #Asymptotic Analysis #Sorting Algorithms #Algorithm Analysis #Dynamic programming #Red-black Trees

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