Introduction to Algorithms and Analysis

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.

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.
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