Design and Analysis of Algorithms

Explore a comprehensive 10-hour course on the Design and Analysis of Algorithms (DAA). Delve into fundamental concepts such as algorithm analysis, asymptotic notations, and time complexities. Master various algorithmic techniques including divide and conquer, greedy methods, and dynamic programming. Learn essential sorting and searching algorithms, data structures like trees and heaps, and graph algorithms such as Dijkstra's and Bellman-Ford. Tackle important topics for GATE/NET exams and placements, including recurrence relations, hashing techniques, and advanced algorithms like Floyd-Warshall. Gain practical knowledge through numerous examples, GATE questions, and in-depth explanations of algorithm performance and complexity analysis.

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