Recursion, Backtracking And Dynamic Programming In Java

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Recursion, Backtracking And Dynamic Programming In Java
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 2.22 GB | Duration: 14h 46m​

Competitive Programming with Common Interview Questions (Recursion, Backtracking and Divide and Conquer Algorithms)

What you'll learn
Understand recursive approaches
Understand backtracking
Understand dynamic programming
Understand divide and conquer methods
Implement 15+ algorithmic problems from scratch
Improve your problem solving skills and become a stronger developer

Requirements
Basic Java

Description
This course is about the fundamental concepts of algorithmic problems focusing on recursion, backtracking, dynamic programming and divide and conquer approaches. As far as I am concerned, these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D.Section 1 - RECURSIONwhat are recursion and recursive methodsstack memory and heap memory overviewwhat is stack overflow?Fibonacci numbersfactorial functiontower of Hanoi problemSection 2 - SEARCH ALGORITHMSlinear search approachbinary search algorithmSection 3 - SELECTION ALGORITHMSwhat are selection algorithms?how to find the k-th order statistics in O(N) linear running time?quickselect algorithmmedian of medians algorithmthe secretary problemSection 4 - BIT MANIPULATION PROBLEMSbinary numberslogical operators and shift operatorschecking even and odd numbersbit length problemRussian peasant multiplicationSection 5 - BACKTRACKINGwhat is backtracking?n-queens problemHamiltonian cycle problemcoloring problemknight's tour problemSudoku gameSection 6 - DYNAMIC PROGRAMMINGwhat is dynamic programming?knapsack problemrod cutting problemsubset sum problemKadan's algorithm (maximum subarray)longest common subsequence (LCS) problem Section 7 - OPTIMAL PACKING what is optimal packing?bin packing problemSection 8 - DIVIDE AND CONQUER APPROACHESwhat is the divide and conquer approach?dynamic programming and divide and conquer methodhow to achieve sorting in O(NlogN) with merge sort?the closest pair of points problemSection 9 - COMMON INTERVIEW QUESTIONStop interview questions (Google, Facebook and Amazon)anagram problempalindrome problemtrapping rain water problemegg dropping problemdutch national flag problemIn each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together from scratch in Java.Finally, YOU CAN LEARN ABOUT THE MOST COMMON INTERVIEW QUESTIONS (Google, Microsoft, Amazon etc.)Thanks for joining the course, let's get started!

Who this course is for:
This course is meant for newbies who are not familiar with algorithmic problems in the main or students looking for some refresher,Anyone preparing for programming interviews or interested in improving their problem solving skills

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[0dayddl]

Recursion, Backtracking In Programming
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 226.05 MB | Duration: 0h 55m​

Learn Recursion, Backtracking in Programming and be prepared for interviews!

What you'll learn

Understanding Recursion techniques

Understand the data structures

Implement backtracking methods

Improve problems solving skills

Requirements

Must have any basics knowledge in any programming language

Description

You are going to learn Recursion, Backtracking in Programming. Recursion and backtracking are two closely related concepts in programming that play a crucial role in solving complex problems efficiently. They are widely used in algorithms involving search, enumeration, and optimization. Understanding these concepts is essential for mastering problem-solving techniques in data structures and algorithms.Recursion is a problem-solving technique in which a function calls itself to break down a problem into smaller subproblems of the same type. This approach is useful for tasks that can be divided into identical but smaller tasks, such as computing factorials, traversing trees, and solving mathematical series. A recursive function typically consists of two key components: a base case, which terminates the recursion, and a recursive case, which reduces the problem size by calling the function itself with modified parameters.Backtracking is an advanced form of recursion used for systematic searching through possible solutions. It is a depth-first approach where a partial solution is built incrementally, and whenever a constraint is violated, the algorithm discards the current path and backtracks to the previous step. This method is particularly useful for combinatorial problems, such as the N-Queens problem, Sudoku solving, and generating permutations.Recursion and backtracking are powerful techniques that simplify complex problems. While recursion helps break problems into smaller parts, backtracking enables efficient searching by eliminating unfeasible solutions. Together, they form the backbone of many algorithmic solutions in computing.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Backtracking blueprint

Section 2: Method and Complexity Analysis

Lecture 3 Method and Complexity Analysis

Lecture 4 Recursion methods

Section 3: Recursion Tree

Lecture 5 Recursion Tree

Lecture 6 Complexity Analysis in Backtracking

Section 4: Runcode Compalization

Lecture 7 Runcode Compalization

It is for beginners who are not aware of data structures and algorithmic topics

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