Design and Analysis of Algorithms

Course Curriculum

Chapter 1: Introduction to Algorithms

Lecture 1: Introduction to the Concept of Algorithms

Lecture 2: Definition of an algorithm

Lecture 3: Algorithm specification – Pseudocode Convention

Lecture 4: Performance Analysis Space complexity Time Complexity

Lecture 5: Asymptotic notations

Lecture 6: Randomized Algorithms

Chapter 2: Divide and Conquer method

Lecture 1: General Method

Lecture 2: Binary Search

Lecture 3: Merge Sort

Lecture 4: Quicksort

Lecture 5: Merge sort and Quick sort-Complexity

Lecture 6: Merge sort vs Quick sort

Lecture 7: Strassen's Matrix Multiplication

Chapter 3: Disjoint Sets and Representations

Lecture 1: Introduction

Lecture 2: Set Operations

Lecture 3: Graph Definition and Depth First Search traversal method

Lecture 4: Breadth First Search traversal Algorithm

Lecture 5: Bi-Connected Components

Lecture 6: Finding Articulation points in a Connected Component

Chapter 4: Greedy Method

Lecture 1: Greedy Method and Control Abstraction

Lecture 2: Knapsack Problem-Greedy Solution

Lecture 3: Job Sequencing with deadlines

Lecture 4: Prim's Algorithm Example

Lecture 5: Kruskal's Algorithm with an example

Lecture 6: Single Source Shortest path problem (Dijkstra's Algorithm)

Chapter 5: Dynamic Programming and its applications.

Lecture 1: Dynamic Programming and Principle of optimality

Lecture 2: 0/1 Knapsack Problem

Lecture 3: Matrix chain multiplication

Lecture 4: Travelling salesperson problem

Lecture 5: Reliability Design

Lecture 6: Optimal Binary Search Tree problem

Chapter 6: Backtracking and its applications

Lecture 1: Introduction to Backtracking.

Lecture 2: N-Queen problem

Lecture 3: Graph Coloring problem

Lecture 4: Hamiltanian Cycle problem

Chapter 7: Branch and Bound Applications.

Lecture 1: LC-Branch and Bound

Lecture 2: FIFO-Branch and Bound