Design and Analysis of algorithms (DAA) (Hindi)

   Friday, September 27, 2024   

Starting a new Lecture Notes Series on Design and Analysis of algorithms (DAA)

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Design and Analysis of algorithms (DAA) By Lecture Notes together!

Lecture 1: L-1.1: Introduction to Algorithm & Syllabus Discussion for GATE/NET & Placements Preparation | DAA

Lecture 2: L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA

Lecture 3: L-1.3: Asymptotic Notations | Big O | Big Omega | Theta Notations | Most Imp Topic Of Algorithm

Lecture 4: L-1.4: Various Properties of Asymptotic Notation with Example | Algorithm | DAA

Lecture 5: L-1.5: Comparison of Various Time Complexities | Different types in Increasing Order| Must Watch

Lecture 6: L-1.6: Time Complexities of all Searching and Sorting Algorithms in 10 minute | GATE & other Exams

Lecture 7: L-1.7: Question#1 on Comparison of Various Time Complexities | GATE Questions

Lecture 8: L-1.8: Question#2 on Comparison of Various Time Complexities | GATE Questions

Lecture 9: L-2.1: What is Recurrence Relation| How to Write Binary Search Recurrence Relation|How we Solve them

Lecture 10: L-2.2: Recurrence Relation [ T(n)= T(n/2) + c] | Substitution Method | Algorithm

Lecture 11: L-2.3: Recurrence Relation [ T(n)= n*T(n-1) ] | Substitution Method | Algorithm

Lecture 12: L-2.4: Recurrence Relation [ T(n)= 2T(n/2) +n] | Substitution Method | Algorithm

Lecture 13: L-2.5: Recurrence Relation [ T(n)= T(n-1) +logn] | Substitution Method | Algorithm

Lecture 14: L-2.6: Recurrence Relation [ T(n)= 8T(n/2) + n^2 ] | Master Theorem | Example#1 | Algorithm

Lecture 15: L-2.7: Recurrence Relation [ T(n)= T(n/2) +c] | Master Theorem | Example-2 | Algorithm

Lecture 16: L-2.8: Recurrence Relation T(n)=T(√n)+logn | Master Theorem

Lecture 17: L-2.9: Recurrence Relation [T(n)= 2T(n/2) +cn] | Recursive Tree method | Algorithm

Lecture 18: L-2.10: Recurrence Relation [T(n)= 3T(n/4) +cn^2] | Recursive Tree method | Algorithm

Lecture 19: L-3.0: Divide and Conquer | Algorithm

Lecture 20: L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer

Lecture 21: L-3.2: Performance of Quick Sort | Worst Case Time Complexity with Example | Algorithm

Lecture 22: How Merge Sort Works?? Full explanation with example

Lecture 23: Merge Sort Pseudocode | Merge Sort with example

Lecture 24: L-3.3: Imp. Question on Merge Sort | Divide and Conquer | Algorithm

Lecture 25: L-3.4: How Bubble Sort Works | Performance of Bubble Sort | All Imp Points with Example | Algorithm

Lecture 26: L-3.5: Insertion Sort | Time Complexity Analysis | Stable Sort | Inplace Sorting

Lecture 27: L-3.6: Selection Sort | Time Complexity(Best, Avg & Worst) Analysis | Stable or Not | Inplace or Not

Lecture 28: Radix Sort | Easiest explanation with example

Lecture 29: Counting Sort | Easiest explanation with example

Lecture 30: Bucket Sort | Easiest explanation with examples | Time Complexity analysis with Pseudocode

Lecture 31: L-3.7: Introduction to Trees (Binary Tree, Almost Complete Binary Tree, Full BT, Complete BT, BST)

Lecture 32: L-3.8: Introduction to Heap Tree with examples | Max Min Heap

Lecture 33: L-3.9: Insertion in Heap Tree | Max-Heap & Min-Heap Creation | Time Complexities

Lecture 34: L-3.10: Imp Question on Max Heap | GATE Question on Max/Min Heap | Algorithm

Lecture 35: L-3.11: Build Heap in O(n) time complexity | Heapify Method | Full Derivation with example

Lecture 36: L-3.12: Deletion in Heap tree | Time complexity

Lecture 37: L-3.13: Heap sort with Example | Heapify Method

Lecture 38: L-4.1: Introduction to Greedy Techniques With Example | What is Greedy Techniques

Lecture 39: L-4.2: Knapsack Problem With Example| Greedy Techniques| Algorithm

Lecture 40: L-4.3: Huffman Coding Algorithm in Hindi with Example | Greedy Techniques(Algorithm)

Lecture 41: L-4.4: Huffman Coding Question in Greedy Technique | Imp Question for all competitive exams

Lecture 42: L-4.5: Job Sequencing Algorithm with Example | Greedy Techniques

Lecture 43: L-4.6: Optimal Merge Pattern using Greedy Method in Hindi | Algorithm

Lecture 44: L-4.7: What is Spanning Tree with Examples in Hindi | Algorithm

Lecture 45: L-4.8: Kruskal Algorithm for Minimum Spanning Tree in Hindi | Algorithm

Lecture 46: L-4.9: Prim's Algorithm for Minimum Cost Spanning Tree | Prims vs Kruskal

Lecture 47: L-4.10: Dijkstra's Algorithm - Single Source Shortest Path - Greedy Method

Lecture 48: L-4.11: Dijkstra's Algorithm Analysis | Time Complexity | Pseudocode Explanation

Lecture 49: L-4.12: Why does Dijkstra fail on Negative Weights?? Full Explanation with examples

Lecture 50: L-4.13: Bellman Ford Algorithm | Dijkstra's Vs Bellman Ford | Single Source Shortest Path

Lecture 51: L-4.14: Bellman Ford pseudo code and Time complexity | Single Source Shortest Path

Lecture 52: L-4.15: BFS & DFS | Breadth First Search | Depth First Search | Graph Traversing | DAA

Lecture 53: L-5.1: Introduction to Dynamic Programming | Greedy Vs Dynamic Programming | Algorithm(DAA)

Lecture 54: L-5.2: 0/1 Knapsack failed using Greedy approach

Lecture 55: L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree Time Complexity

Lecture 56: L-5.4: Traveling Salesman Problem | Dynamic Programming

Lecture 57: L-5.5: Sum of Subsets Problem | Dynamic Programming

Lecture 58: L-5.6: Multistage Graph | Dynamic Programming

Lecture 59: L-5.7: Introduction to All Pair Shortest Path (Floyd Warshall Algorithm)

Lecture 60: L-5.8: Floyd Warshall Working with example | All Pair Shortest Path Algorithm

Lecture 61: L-5.9: Floyd Warshall Time & Space complexity | All Pair Shortest Path

Lecture 62: L-6.1: What is hashing with example | Hashing in data structure

Lecture 63: L-6.2: Collision Resolution Techniques in Hashing | What are the collision resolution techniques?

Lecture 64: L-6.3: Chaining in Hashing | What is chaining in hashing with examples

Lecture 65: L-6.4: Linear Probing in Hashing with example

Lecture 66: L-6.5: Imp Question on Hashing | Linear Probing for Collision in Hash Table | GATE Questions

Lecture 67: L-6.6: Quadratic Probing in Hashing with example

Lecture 68: L-6.7: Double Hashing | Collision Resolution Technique

Lecture 69: Topological Sorting with examples | Topological Sorting using DFS | Imp For Placements & Comp. Exams

Lecture 61: Kahn's Algorithm | Topological Sorting | DAA | Imp for Placements

Lecture 62: Branch & Bound Algorithm with Example | Easiest Explanation of B&B with example

Lecture 63: 0/1 Knapsack using Branch and Bound with example

Lecture 64: Shortcut for Swaps🔁 in Bubble🫧 Sorting📶| Best Case | Worst Case

Lecture 65: Optimized Bubble Sort | Best Case | O(n) Time Complexity | Sorting Algorithms |DAA

Lecture 66: Recurrence Relation [T(n) = 2T(n/2) + 2] | Min-Max Algorithm

Lecture 67: Min-Max Algorithm without Divide & Conquer🏆 | Linear Approach | Find Max & Min

Lecture 68: Min Max Algorithm with Divide & Conquer🏆

Lecture 69: Time complexity and Comparison of All Data Structures