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Analysis of Algorithm

Simply Easy Learning

Created by Arnab Chakraborty, Last Updated 07-Aug-2019, Language:English

Description

Analysis of Algorithm is an important part of a broader computational complexity theory, which provides theoretical estimates for the resources needed by any algorithm which solves a given computational problem. These estimates provide an insight into reasonable directions of search for efficient algorithms. This video tutorial will give you a great understanding on Analysis of Algorithm needed to understand the complexity of enterprise level applications and need of algorithms, and data structures.

Audience

This video tutorial is designed for students interested in learning Analysis of Algorithm and its applications. Enthusiastic readers who would like to know more about algorithms and those who wish to implement algorithms themselves may start from here. These video classes have been developed based on the latest GATE syllabus and will be useful for undergraduate students of Computer Science and Information Technology as well as those preparing for GATE exams. It will also be helpful for students in preparing them for their Engineering Syllabus.

Course Content

  • Analysis of Algorithm 1 Lectures 00:01:03

    • Analysis of Algorithms - Getting Started

      Preview
      00:01:03
  • Algorithm Introduction 1 Lectures 00:03:22

    • Analysis of Algorithm - Definition

      Preview
      00:03:22
  • Complexity of Algorithm 7 Lectures 00:26:52

    • Space and Time Complexity of an Algorithm

      Preview
      00:06:39

    • Time Complexity Big-Oh Notation

      Preview
      00:04:04

    • Time Complexity Big-Omega Notation

      00:03:59

    • Time Complexity Big-Theta Notation

      00:04:42

    • Asymptotic Notations GATE Exercise 1

      00:01:54

    • Asymptotic Notations GATE Exercise 2

      00:02:34

    • Time Complexity GATE Exercise

      00:03:00
  • Algorithm and Classification 9 Lectures 00:33:11

    • Algorithm Classification - Introduction

      00:03:08

    • Algorithm Classification - Simple Recursive Algorithm

      00:03:12

    • Algorithm Classification - Back Tracking Algorithm

      00:04:17

    • Algorithm Classification - Divide and Conquer

      00:02:17

    • Algorithm Classification - Dynamic Programming

      Preview
      00:03:14

    • Algorithm Classification - Greedy Algorithm

      00:06:25

    • Algorithm Classification - Branch and Bound

      00:05:09

    • Algorithm Classification - Brute Force

      00:03:41

    • Algorithm Classification - Randomized Algorithm

      00:01:48
  • Elementary Data Structure 20 Lectures 01:28:26

    • Definition of Data Structure

      00:03:09

    • Classification of Data Structure

      00:02:48

    • Lower Triangular Sparse Matrix

      00:06:16

    • Tridiagonal Sparse Matrix

      00:07:28

    • Sparse Matrix Another Example

      00:04:31

    • Array GATE Exercise

      00:02:30

    • Stack GATE Exercise

      00:03:48

    • Binary Tree GATE Exercise

      00:03:15

    • Binary Tree Traversals GATE Exercise 1

      00:04:11

    • Binary Tree Traversals GATE Exercise 2

      00:03:42

    • Binary Search Tree Introduction

      00:06:18

    • Ranked Binary Search Tree

      Preview
      00:06:29

    • Binary Search Tree GATE Exercise 1

      00:03:44

    • Binary Search Tree GATE Exercise 2

      00:02:12

    • AVL Search Tree GATE Exercise

      00:07:16

    • B-Tree - Definition and Properties

      00:06:29

    • B-Tree - An Example

      00:08:38

    • Sorting GATE Exercise 1

      00:01:47

    • Sorting GATE Exercise 2

      00:01:21

    • Hashing GATE Exercise

      00:02:34
  • Sets and Set Operations 4 Lectures 00:31:10

    • Sets and Disjoint Set Union

      00:04:45

    • Simple Set Union and Simple Set Find Algorithms

      00:10:37

    • Weighted Union Algorithm

      00:08:52

    • Collapsing Find Algorithm

      00:06:56
  • Recursion 10 Lectures 00:52:41

    • Definition of Recursion

      00:09:01

    • Recursion Variations Direct

      Preview
      00:05:02

    • Recursion Variations Indirect

      00:01:54

    • Recursion Variations Nested

      00:05:29

    • Recursion Variations Excessive

      00:05:37

    • Recursion Variations Tail

      00:04:21

    • Recursion Overheads

      00:03:04

    • Towers of Hanoi as an Example of Recursion

      00:11:02

    • Recursion GATE Exercise 1

      00:03:01

    • Recursion GATE Exercise 2

      00:04:10
  • Divide and Conquer 4 Lectures 00:28:17

    • Divide and Conquer Algorithm

      00:04:41

    • Quick Sort

      00:08:38

    • Merge Sort

      00:07:21

    • Execution Tracing of Merge Sort

      00:07:37
  • The Greedy Method 5 Lectures 00:30:20

    • Properties of Greedy Algorithm

      00:06:25

    • A Sample Greedy Algorithm

      00:05:43

    • Greedy Method Job Sequencing With Deadline

      00:07:49

    • Greedy Algorithm GATE Exercise

      00:04:29

    • Greedy Method Job Sequencing With Deadline Example

      00:05:54
  • Dynamic Programming 7 Lectures 00:54:47

    • Dynamic Programming Matrix Chain Multiplication

      00:09:49

    • Dynamic Programming Matrix Chain Multiplication - Example

      00:06:20

    • Matrix Multiplication GATE Exercise

      00:03:20

    • Dynamic Programming Travelling Salesman Problem

      00:07:38

    • Dynamic Programming Travelling Salesman Problem - Example

      00:09:13

    • Knapsack Problem

      00:07:13

    • Knapsack Algorithm and Example

      00:11:14
  • Back Tracking Algorithm 7 Lectures 00:54:45

    • Back Tracking Algorithm N-Queen’s Problem

      00:07:10

    • Back Tracking Algorithm 8-Queen’s Problem

      00:04:31

    • Back Tracking Algorithm N-Queen’s Algorithm

      00:07:42

    • Back Tracking Hamiltonian Cycles

      00:03:46

    • Back Tracking Algorithm For Hamiltonian Cycles

      00:11:10

    • Back Tracking Graph Coloring Problem

      00:11:02

    • Back Tracking Graph Coloring Algorithm

      00:09:24
  • Branch and Bound 2 Lectures 00:15:32

    • Branch and Bound 15-Puzzle Problem

      00:05:43

    • Branch and Bound 15-Puzzle Problem Game Tree

      00:09:49
  • NP-Hard and NP - Complete Problems 3 Lectures 00:18:15

    • NP-Hard and NP - Complete Problems

      00:05:55

    • Non-Deterministic Algorithms

      00:08:05

    • Some Important Issues on P And NP Algorithms

      00:04:15

Arnab Chakraborty

Corporate Trainer

Prof. Arnab Chakraborty is a Calcutta University alumnus with B.Sc. in Physics Hons Gold medalist, B. Tech and M. Tech in Computer Science and Engineering has twenty-six+ years of academic teaching experience in different universities, colleges and thirteen+ years of corporate training experiences for 170+ companies and trained 50,000+ professionals. He has also completed MBA from Vidyasagar University with dual specialization in Human Resource Management and Marketing Management. He is NLP and PMP trained, "Global DMAIC Six Sigma Master Black Belt" certified by IQF (USA). He is certified by ISA (USA) on "Control and Automation System". He is "Global ITIL V3 Foundation" certified as awarded by APMG (UK). Qualified for "Accredited Management Teacher" by AIMA (India). "Star Python" Global Certified from Star Certification (USA). "Certified Scrum Master (CSM)" Global Certification from Scrum Alliance (USA). He is also empaneled trainer for multiple corporates, e.g. HP, Accenture, IBM etc