Algorithm
Course Overview
HTML is a good Language
Topics
Introduction to Algorithms
Priori Analysis and Posteriori Testing
Characteristics of Algorithm
How Write and Analyze Algorithm
Frequency Count Method
Time Complexity
Time Complexity Example
Time Complexity of While and if
Classes of functions
Compare Class of Functions
Asymptotic Notations Big Oh - Omega - Theta
Asymptotic Notations - Big Oh - Omega - Theta
Properties of Asymptotic Notations
Comparison of Functions
Comparison of Functions
Best Worst and Average Case Analysis
Disjoint Sets Data Structure - Weighted Union and Collapsing Find
Divide And Conquer
Recurrence Relation (T(n)= T(n-1) + 1)
Recurrence Relation (T(n)= T(n-1) + n)
Recurrence Relation (T(n)= T(n-1) + log n)
Recurrence Relation T(n)=2 T(n-1)+1
Masters Theorem Decreasing Function
Recurrence Relation Dividing Function T(n)=T(n/2)+1
Recurrence Relation Dividing [ T(n)=T(n/2)+ n]
Recurrence Relation [ T(n)= 2T(n/2) +n]
Masters Theorem in Algorithms for Dividing Function
Examples for Master Theorem
Root function (Recurrence Relation)
Binary Search Iterative Method
Binary Search Recursive Method
Heap - Heap Sort - Heapify - Priority Queues
Two Way MergeSort - Iterative method
Merge Sort Algorithm
MergeSort in-depth Analysis
QuickSort Algorithm
QuickSort Analysis
Strassens Matrix Multiplication
Greedy Method - Introduction
Knapsack Problem - Greedy Method
Job Sequencing with Deadlines - Greedy Method
Optimal Merge Pattern - Greedy Method
Huffman Coding - Greedy Method
Prims and Kruskals Algorithms - Greedy Method
Dijkstra Algorithm - Single Source Shortest Path - Greedy Method
Principle of Optimality - Dynamic Programming introduction
MultiStage Graph - Dynamic Programming
MultiStage Graph (Program) - Dynamic Programming
All Pairs Shortest Path (Floyd-Warshall) - Dynamic Programming
Matrix Chain Multiplication - Dynamic Programming
Matrix Chain Multiplication using Dynamic Programming Formula
Matrix Chain Multiplication (Program) - Dynamic Programming
Bellman Ford Algorithm - Single Source Shortest Path - Dynamic Programming
Knapsack - Two Methods - Dynamic Programming
Knapsack Problem (Program) - Dynamic Programming
Optimal Binary Search Tree (Successful Search Only) - Dynamic Programming
Optimal Binary Search Tree Successful and Unsuccessful Probability - Dynamic Programming
Traveling Salesman Problem - Dynamic Programming using Formula
Reliability Design - Dynamic Programming
Longest Common Subsequence (LCS) - Recursion and Dynamic Programming
Graph Traversals - BFS & DFS -Breadth First Search and Depth First Search
Articulation Point and Biconnected Components
Introduction to Backtracking - Brute Force Approach
N Queens Problem using Backtracking
Sum Of Subsets Problem - Backtracking
Graph Coloring Problem - Backtracking
Hamiltonian Cycle - Backtracking
Branch and Bound Introduction
Job Sequencing with Deadline - Branch and Bound
Knapsack using Branch and Bound
Traveling Salesman Problem - Branch and Bound
NP-Hard and NP-Complete Problems
NP-Hard Graph Problem - Clique Decision Problem
Knuth-Morris-Pratt KMP String Matching Algorithm
Rabin-Karp String Matching Algorithm
AVL Tree - Insertion and Rotations
B Trees and B+ Trees. How they are useful in Databases
Asymptotic Notations - Simplified
Hashing Technique - Simplified
Shortest Path Algorithms (Dijkstra and Bellman-Ford) - Simplified
BFS DFS - Simplified
Tower of Hanoi Problem - Made Easy
Row-Major and Column-Major Mapping
Merge Sort Algorithm