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