Algorithm Design Techniques From beginner to advanced (DAA)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Over view

Lecture 2: Math Fundamentals

Lecture 3: Introduction

Lecture 4: Space Complexity

Chapter 2: Comnplexity Calculation of simple algorithms

Lecture 1: Elementary operations and computationof time complexity

Lecture 2: Order of growth or rate of growth

Lecture 3: Timecomlexity computation of simple algorithms

Lecture 4: Why do we analyse algorithms?

Lecture 5: Time complexity computation more examples

Lecture 6: Best Worst and Average case complexity

Lecture 7: Complexity calculation of binary search algorithm

Lecture 8: Complexity calculation of bubble sort

Lecture 9: Asymtotic Notations

Chapter 3: Complexity Calculation of recursive Algorithms

Lecture 1: Recursive Factorial

Lecture 2: Recursive binary search

Lecture 3: nth Fibonacci number

Lecture 4: Solution to recurrence Equation: Iterative Method: Example 1

Lecture 5: Solution to recurrence Equation: Iterative Method: Example 2

Lecture 6: Solution to recurrence Equation: Iterative Method: Example 3

Lecture 7: Solution to recurrence equation using recursion tree method: Example 1

Lecture 8: Solution to recurrence equation using recursion tree method: Example 2

Lecture 9: Solution to recurrence equation using recursion tree method: Example 3

Lecture 10: Solution to recurrence equation using recursion tree method: Example 4

Lecture 11: Master's method

Chapter 4: Binary Search Tree

Lecture 1: Need of Binary Search Tree

Lecture 2: Introduction to Binary search tree

Lecture 3: Searching in BST

Lecture 4: Insertion in BST

Lecture 5: Deletion in BST

Lecture 6: Disadvantages of BST

Chapter 5: A V L Tree

Lecture 1: Introduction

Lecture 2: AVL Tree Rotations

Lecture 3: AVL Tree Insertion

Lecture 4: AVL Tree Deletion

Chapter 6: Red Black Tree

Lecture 1: Introduction

Lecture 2: Insertion

Lecture 3: Deletion

Chapter 7: m-way search tree

Lecture 1: Introduction

Lecture 2: B Tree

Lecture 3: B Tree Insertion

Lecture 4: B Tree Deletion

Chapter 8: Disjoint Set

Lecture 1: Introduction

Lecture 2: Disjoint Set Operation : Union

Lecture 3: Disjoint Set Operation : Find

Chapter 9: Graph searching

Lecture 1: Breadth First Search (B F S)

Lecture 2: Analysis of BFS Algorithm

Lecture 3: Depth First Search (D F S)

Lecture 4: Properties of DFS

Lecture 5: Analysis of DFS Algorithm

Lecture 6: Topological sorting

Lecture 7: Strongly connected components

Chapter 10: Minimum Spanning tree Algorithm

Lecture 1: Introduction

Lecture 2: Kruskal's algorithm

Lecture 3: Analysis of Kruskal's algorithm

Lecture 4: Prim's algorithm

Lecture 5: Analysis of Prim's algorithm

Lecture 6: Comparison of Prim's and Kruskal's algorithm

Chapter 11: Single source shortest path problem

Lecture 1: Introduction

Lecture 2: Bellman-Ford algorithm

Lecture 3: Dijkstra's algorithm

Lecture 4: Detection of negative wight cycle

Lecture 5: Comparison of Belman-Ford and Kruskal's algorithm

Chapter 12: Divide and Conquer algorithms

Lecture 1: Control abstraction

Lecture 2: Merge sort

Lecture 3: Merge sort algorithm

Lecture 4: Analysis of merge sort

Lecture 5: Matrix multiplication

Lecture 6: Divide and Conquer matrix multiplication

Lecture 7: Strassan's matrix multiplication

Chapter 13: Dynamic programming

Lecture 1: Control abstraction

Lecture 2: The matrix chain multiplication problem

Lecture 3: Matrix chain multiplication example

Lecture 4: Matrix chain multiplication algorithm

Lecture 5: Comparison of Divide and conquer and Dynamic programming techniques

Chapter 14: Greedy strategy

Lecture 1: The Control Abstraction

Lecture 2: Fractional Knapsack Problem

Lecture 3: Greedy algorithm for fractional knapsack problem

Chapter 15: Backtracking Strategy

Lecture 1: The 4 Queen problem

Lecture 2: Solving 4 queen problem using back tracking technique

Lecture 3: Some terminologies