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Design and Analysis of Algorithms

Design and Analysis of Algorithms

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

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

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Design and Analysis of Algorithms

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  1. 1 L-1.1: Introduction to Algorithm & Syllabus Discussion for GATE/NET & Placements Preparation | DAA
  2. 2 L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA
  3. 3 L-1.3: Asymptotic Notations | Big O | Big Omega | Theta Notations | Most Imp Topic Of Algorithm
  4. 4 L-1.4: Various Properties of Asymptotic Notation with Example | Algorithm | DAA
  5. 5 L-1.5: Comparison of Various Time Complexities | Different types in Increasing Order| Must Watch
  6. 6 L-1.6: Time Complexities of all Searching and Sorting Algorithms in 10 minute | GATE & other Exams
  7. 7 L-1.7: Question#1 on Comparison of Various Time Complexities | GATE Questions
  8. 8 L-1.8: Question#2 on Comparison of Various Time Complexities | GATE Questions
  9. 9 L-2.1: What is Recurrence Relation| How to Write Binary Search Recurrence Relation|How we Solve them
  10. 10 L-2.2: How to Solve Recurrence Relation using Substitution Method | Question#2 | Algorithm
  11. 11 L-2.3: What is Substitution Method| How to Solve Recurrence Relation using Substitution Method
  12. 12 L-2.4: How Master Theorem Solve Recurrence Relations| Example#1 | All Cases Explained with Example
  13. 13 L-2.5: How to Solve Recurrence Relation Using Master Method | Example-2 | Master Theorem | Algorithm
  14. 14 L-3.0: Divide and Conquer | Algorithm
  15. 15 L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer
  16. 16 L-3.2: Performance of Quick Sort | Worst Case Time Complexity with Example | Algorithm
  17. 17 L-3.3: Imp. Question on Merge Sort | Divide and Conquer | Algorithm
  18. 18 L-3.4: How Bubble Sort Works | Performance of Bubble Sort | All Imp Points with Example | Algorithm
  19. 19 L-3.5: Insertion Sort | Time Complexity Analysis | Stable Sort | Inplace Sorting
  20. 20 L-3.6: Selection Sort | Time Complexity(Best, Avg & Worst) Analysis | Stable or Not | Inplace or Not
  21. 21 L-3.7: Introduction to Trees (Binary Tree, Almost Complete Binary Tree, Full BT, Complete BT, BST)
  22. 22 L-3.8: Introduction to Heap Tree with examples | Max Min Heap
  23. 23 L-3.9: Insertion in Heap Tree | Max-Heap & Min-Heap Creation | Time Complexities
  24. 24 L-3.10: Imp Question on Max Heap | GATE Question on Max/Min Heap | Algorithm
  25. 25 L-3.11: Build Heap in O(n) time complexity | Heapify Method | Full Derivation with example
  26. 26 L-3.12: Deletion in Heap tree | Time complexity
  27. 27 L-3.13: Heap sort with Example | Heapify Method
  28. 28 L-4.1: Introduction to Greedy Techniques With Example | What is Greedy Techniques
  29. 29 L-4.2: Knapsack Problem With Example| Greedy Techniques| Algorithm
  30. 30 L-4.3: Huffman Coding Algorithm in Hindi with Example | Greedy Techniques(Algorithm)
  31. 31 L-4.4: Huffman Coding Question in Greedy Technique | Imp Question for all competitive exams
  32. 32 L-4.5: Job Sequencing Algorithm with Example | Greedy Techniques
  33. 33 L-4.6: Optimal Merge Pattern using Greedy Method in Hindi | Algorithm
  34. 34 L-4.7: What is Spanning Tree with Examples in Hindi | Algorithm
  35. 35 L-4.8: Kruskal Algorithm for Minimum Spanning Tree in Hindi | Algorithm
  36. 36 L-4.9: Prim's Algorithm for Minimum Cost Spanning Tree | Prims vs Kruskal
  37. 37 L-4.10: Dijkstra's Algorithm - Single Source Shortest Path - Greedy Method
  38. 38 L-4.11: Dijkstra's Algorithm Analysis | Time Complexity | Pseudocode Explanation
  39. 39 L-4.12: Why does Dijkstra fail on Negative Weights?? Full Explanation with examples
  40. 40 L-4.13: Bellman Ford Algorithm | Dijkstra's Vs Bellman Ford | Single Source Shortest Path
  41. 41 L-4.14: Bellman Ford pseudo code and Time complexity | Single Source Shortest Path
  42. 42 L-5.1: Introduction to Dynamic Programming | Greedy Vs Dynamic Programming | Algorithm(DAA)
  43. 43 L-5.2: 0/1 Knapsack failed using Greedy approach
  44. 44 L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree|Time Complexity
  45. 45 L-5.4: Traveling Salesman Problem | Dynamic Programming
  46. 46 Sum of Subsets Problem | Dynamic Programming
  47. 47 Multistage Graph | Dynamic Programming
  48. 48 L-6.1: What is hashing with example | Hashing in data structure
  49. 49 L-6.2: Collision Resolution Techniques in Hashing | What are the collision resolution techniques?
  50. 50 L-6.3: Chaining in Hashing | What is chaining in hashing with examples
  51. 51 L-6.4: Linear Probing in Hashing with example
  52. 52 L-6.5: Imp Question on Hashing | Linear Probing for Collision in Hash Table | GATE Questions
  53. 53 L-6.6: Quadratic Probing in Hashing with example
  54. 54 L-6.7: Double Hashing | Collision Resolution Technique
  55. 55 Recurrence Relation T(n)=T(√n)+logn | Master Theorem
  56. 56 Introduction to All Pair Shortest Path (Floyd Warshall Algorithm)
  57. 57 Floyd Warshall Working with example | All Pair Shortest Path Algorithm
  58. 58 Floyd Warshall Time & Space complexity | All Pair Shortest Path

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