Design and Analysis of Algorithms

1. Introduction to Algorithms
2. Complexity Analysis of an Algorithm
3. Let's Understand Big Omega, Big Theta, Small Oh and Small Omega
4. Introduction To NP-Completeness

Divide and Conquer Method

1. Divide and Conquer Technique
2. Binary Search (Divide and Conquer Technique)
3. Quick Sort (Divide and Conquer Technique)
4. Merge Sort (Divide and Conquer Technique)
5. Max-Min Problem (Divide and Conquer Technique)

Dynamic Programming

1. Dynamic Programming Method
2. Matrix Chain Multiplication (Dynamic Programming)
3. Travelling Salesman Problem
4. 0-1 Knapsack Problem using Dynamic Programming
5. Network Flow Algorithm: Ford-Fulkarson’s Algorithm
6. All Pair Shortest Path: Floyd-Warshall Algorithm
7. Finding Longest Common Subsequence (Dynamic Programming)

Greedy Strategy

1. Greedy Method
2. Fractional Knapsack using Greedy Approach
3. Single Source Shortest Path: Dijkstra's Algorithm
4. Single Source Shortest Path: Bellman-Ford Algorithm
5. Minimal Spanning Tree using Kruskal's Algorithm
6. Minimal Spanning Tree using Prim's Algorithm

Backtracking Strategy

1. Backtracking Strategy
2. Sum of Sub-Sets Problem using Backtracking
3. Hamiltonian Cycle using Backtracking
4. N-Queens Problem (Backtracking Method)
5. Graph Coloring (Backtracking Method)