Description

This course covers the fundamental principles of algorithmic problems, with an emphasis on recursion, backtracking, and dynamic programming. Algorithms may be employed (and have multiple applications) in a variety of professions ranging from software engineering to investment banking or research and development, in my opinion.

Section One:

what is recursion stack memory and how to solve the recursion factorial numbers problem
Towers of Hanoi Fibonacci numbers recursion versus iteration

Section two:

What is the n-queens backtracking problem?
Hamiltonian cycle issue
coloring problem with the knight’s tour
NP-complete issues

Section three:

What exactly is dynamic programming?
Fibonacci sequences
knapsack issue
trouble with coin exchange
difficulty with rod cutting

In each section, we will discuss the theoretical basis for all of these algorithms before implementing them one by one.

The first chapter discusses recursion. Why is it important for a computer scientist to understand recursion? Why is stack memory important in recursion? We will look at some recursion-related topics, such as the factorial problem and Fibonacci numbers. The second chapter is about backtracking: we will discuss issues like n-queens, hamiltonian cycles, and coloring difficulties. In the last chapter, we will discuss dynamic programming, beginning with theory and progressing to actual problems such as the Fibonacci sequence problem and the knapsack problem.

Thank you for enrolling in the course; let’s get started!

Who is this course aimed at?
This course is intended for beginners who are unfamiliar with algorithmic issues in general, as well as students who want to brush up on their knowledge.