Design Analysis And Algorithms

Importance of Algorithms and Getting Optimal Solutions using various Methods

What you'll learn

Understand the Fundamentals of Algorithms and Analyze Time and Space Complexity

Apply Divide and Conquer to Algorithm Design

Identify Problems Suited for the Greedy Approach

Apply Backtracking to Classic Problems

Requirements

Analytical Thinking and Logical Reasoning Skills

Description

The Design and Analysis of Algorithms (DAA) course provides a comprehensive foundation in algorithm development, performance evaluation, and complexity analysis. It equips students with the skills to design efficient algorithms and analyze their behavior in terms of time and space requirements using asymptotic notations such as Big O, Theta, and Omega. The course begins with an introduction to algorithmic fundamentals, including problem-solving strategies and basic sorting and searching techniques.Core algorithmic paradigms such as Divide and Conquer, Greedy Methods, Dynamic Programming, Backtracking, and Branch and Bound are explored in depth, enabling students to apply these strategies to various real-world problems. Students will study classical algorithms like Quick Sort, Merge Sort, Dijkstra’s algorithm, Floyd-Warshall, and Kruskal’s and Prim’s algorithms for graph processing.The course also introduces the concept of recurrence relations and methods to solve them, enabling analytical reasoning about recursive algorithms. Advanced topics such as NP-completeness, P vs NP, and computational intractability are covered to help students understand the theoretical limits of algorithmic problem-solving.By the end of the course, students will be able to design optimal solutions, justify their efficiency, and make informed choices between different algorithmic approaches based on specific problem constraints. The course is essential for any computer science or engineering student pursuing careers in software development, data science, or research.

Overview

Section 1: Introduction

Lecture 1 BASICS OF ALGORITHMS AND MATHEMATICS

Lecture 2 Characteristics of an Algorithm

Lecture 3 Asymptotic Notations

Lecture 4 Time Complexity

Lecture 5 Worst, Best and Average Case Analysis of Algorithms

Section 2: Divide-and-conquer

Lecture 6 Divide-and-conquer algorithm

Lecture 7 Binary Search Algorithm Divide and Conquer Approach

Lecture 8 MAX-MIN Problem Using Divide and Conquer

Lecture 9 Merge Sort Divide and Conquer

Section 3: Greedy Method

Lecture 10 Greedy Method

Lecture 11 Activity Selection Problem - GM

Undergraduate Computer Science Students