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Design and Analysis of algorithms (DAA) — Free Course | SyncStudy

Course Syllabus

Complete Lecture Curriculum (79 Lectures)

Full index of topics and video runtimes included in this course.

Total: 13 hrs

01
L-1.1: Introduction to Algorithm & Syllabus Discussion for GATE/NET & Placements Preparation | DAA
11:27
Lesson
02
L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA
7:51
Lesson
03
L-1.3: Asymptotic Notations | Big O | Big Omega | Theta Notations | Most Imp Topic Of Algorithm
14:25
Lesson
04
L-1.4: Various Properties of Asymptotic Notation with Example | Algorithm | DAA
8:56
Lesson
05
L-1.5: Comparison of Various Time Complexities | Different types in Increasing Order| Must Watch
9:41
Lesson
06
L-1.6: Time Complexities of all Searching and Sorting Algorithms in 10 minute | GATE & other Exams
12:52
Lesson
07
L-1.7: Question#1 on Comparison of Various Time Complexities | GATE Questions
6:34
Lesson
08
L-1.8: Question#2 on Comparison of Various Time Complexities | GATE Questions
5:26
Lesson
09
L-2.1: What is Recurrence Relation| How to Write Binary Search Recurrence Relation|How we Solve them
7:15
Lesson
10
L-2.2: Recurrence Relation [ T(n)= T(n/2) + c] | Substitution Method | Algorithm
5:38
Lesson
11
L-2.3: Recurrence Relation [ T(n)= n*T(n-1) ] | Substitution Method | Algorithm
7:40
Lesson
12
L-2.4: Recurrence Relation [ T(n)= 2T(n/2) +n] | Substitution Method | Algorithm
8:57
Lesson
13
L-2.5: Recurrence Relation [ T(n)= T(n-1) +logn] | Substitution Method | Algorithm
10:23
Lesson
14
L-2.6: Recurrence Relation [ T(n)= 8T(n/2) + n^2 ] | Master Theorem | Example#1 | Algorithm
6:34
Lesson
15
L-2.7: Recurrence Relation [ T(n)= T(n/2) +c] | Master Theorem | Example-2 | Algorithm
3:58
Lesson
16
L-2.8: Recurrence Relation T(n)=T(√n)+logn | Master Theorem
6:26
Lesson
17
L-2.9: Recurrence Relation [T(n)= 2T(n/2) +cn] | Recursive Tree method | Algorithm
7:14
Lesson
18
L-2.10: Recurrence Relation [T(n)= 3T(n/4) +cn^2] | Recursive Tree method | Algorithm
9:31
Lesson
19
L-3.0: Divide and Conquer | Algorithm
4:03
Lesson
20
L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer
13:27
Lesson
21
L-3.2: Performance of Quick Sort | Worst Case Time Complexity with Example | Algorithm
8:16
Lesson
22
L-3.3: How Merge Sort Works?? Full explanation with example
9:52
Lesson
23
L -3.4: Merge Sort Pseudocode | Merge Sort with example
8:09
Lesson
24
L-3.5: Imp. Question on Merge Sort | Divide and Conquer | Algorithm
6:00
Lesson
25
L-3.6: How Bubble Sort Works | Performance of Bubble Sort | All Imp Points with Example | Algorithm
8:34
Lesson
26
L-3.7: Insertion Sort | Time Complexity Analysis | Stable Sort | Inplace Sorting
15:32
Lesson
27
L-3.8: Selection Sort | Time Complexity(Best, Avg & Worst) Analysis | Stable or Not | Inplace or Not
11:37
Lesson
28
L-3.9: Radix Sort | Easiest explanation with example
6:05
Lesson
29
L-3.10: Counting Sort | Easiest explanation with example
7:37
Lesson
30
L-3.11: Bucket Sort | Easiest explanation with examples | Time Complexity analysis with Pseudocode
10:21
Lesson
31
L-3.12: Introduction to Trees (Binary Tree, Almost Complete Binary Tree, Full BT, Complete BT, BST)
10:45
Lesson
32
L-3.13: Introduction to Heap Tree with examples | Max Min Heap
7:45
Lesson
33
L-3.14: Insertion in Heap Tree | Max-Heap & Min-Heap Creation | Time Complexities
11:33
Lesson
34
L-3.15: Imp Question on Max Heap | GATE Question on Max/Min Heap | Algorithm
10:19
Lesson
35
L-3.16: Build Heap in O(n) time complexity | Heapify Method | Full Derivation with example
21:05
Lesson
36
L-3.17: Deletion in Heap tree | Time complexity
6:15
Lesson
37
L-3.18: Heap sort with Example | Heapify Method
13:38
Lesson
38
L-4.1: Introduction to Greedy Techniques With Example | What is Greedy Techniques
7:32
Lesson
39
L-4.2: Knapsack Problem With Example| Greedy Techniques| Algorithm
11:41
Lesson
40
L-4.3: Huffman Coding Algorithm in Hindi with Example | Greedy Techniques(Algorithm)
12:30
Lesson
41
L-4.4: Huffman Coding Question in Greedy Technique | Imp Question for all competitive exams
7:50
Lesson
42
L-4.5: Job Sequencing Algorithm with Example | Greedy Techniques
10:39
Lesson
43
L-4.6: Optimal Merge Pattern using Greedy Method in Hindi | Algorithm
10:51
Lesson
44
L-4.7: What is Spanning Tree with Examples in Hindi | Algorithm
7:12
Lesson
45
L-4.8: Kruskal Algorithm for Minimum Spanning Tree in Hindi | Algorithm
11:17
Lesson
46
L-4.9: Prim's Algorithm for Minimum Cost Spanning Tree | Prims vs Kruskal
9:55
Lesson
47
L-4.10: Dijkstra's Algorithm - Single Source Shortest Path - Greedy Method
15:49
Lesson
48
L-4.11: Dijkstra's Algorithm Analysis | Time Complexity | Pseudocode Explanation
11:23
Lesson
49
L-4.12: Why does Dijkstra fail on Negative Weights?? Full Explanation with examples
7:54
Lesson
50
L-4.13: Bellman Ford Algorithm | Dijkstra's Vs Bellman Ford | Single Source Shortest Path
16:32
Lesson
51
L-4.14: Bellman Ford pseudo code and Time complexity | Single Source Shortest Path
7:50
Lesson
52
L-4.15: BFS & DFS | Breadth First Search | Depth First Search | Graph Traversing | DAA
11:16
Lesson
53
L-5.1: Introduction to Dynamic Programming | Greedy Vs Dynamic Programming | Algorithm(DAA)
9:08
Lesson
54
L-5.2: 0/1 Knapsack failed using Greedy approach
9:54
Lesson
55
L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree Time Complexity
17:04
Lesson
56
L-5.4: Traveling Salesman Problem | Dynamic Programming
14:12
Lesson
57
L-5.5: Sum of Subsets Problem | Dynamic Programming
15:06
Lesson
58
L-5.6: Multistage Graph | Dynamic Programming
17:32
Lesson
59
L-5.7: Introduction to All Pair Shortest Path (Floyd Warshall Algorithm)
6:24
Lesson
60
L-5.8: Floyd Warshall Working with example | All Pair Shortest Path Algorithm
25:31
Lesson
61
L-5.9: Floyd Warshall Time & Space complexity | All Pair Shortest Path
9:14
Lesson
62
L-6.1: What is hashing with example | Hashing in data structure
5:53
Lesson
63
L-6.2: Collision Resolution Techniques in Hashing | What are the collision resolution techniques?
5:33
Lesson
64
L-6.3: Chaining in Hashing | What is chaining in hashing with examples
6:42
Lesson
65
L-6.4: Linear Probing in Hashing with example
12:40
Lesson
66
L-6.5: Imp Question on Hashing | Linear Probing for Collision in Hash Table | GATE Questions
9:29
Lesson
67
L-6.6: Quadratic Probing in Hashing with example
7:41
Lesson
68
L-6.7: Double Hashing | Collision Resolution Technique
10:28
Lesson
69
L- 6.8:Topological Sorting with examples | Topological Sorting using DFS
11:33
Lesson
70
L-6.9:Kahn's Algorithm | Topological Sorting | DAA | Imp for Placements
7:31
Lesson
71
Lec-41: Branch & Bound Algorithm with Example | Easiest Explanation of B&B with example
13:13
Lesson
72
Lec-42 : 0/1 Knapsack using Branch and Bound with example
11:20
Lesson
73
Shortcut for Swaps🔁 in Bubble🫧 Sorting📶| Best Case | Worst Case
3:06
Lesson
74
Optimized Bubble Sort | Best Case | O(n) Time Complexity | Sorting Algorithms |DAA
8:32
Lesson
75
Recurrence Relation [T(n) = 2T(n/2) + 2] | Min-Max Algorithm
10:05
Lesson
76
Min-Max Algorithm without Divide & Conquer🏆 | Linear Approach | Find Max & Min
6:46
Lesson
77
Min Max Algorithm with Divide & Conquer🏆
8:37
Lesson
78
Time complexity and Comparison of All Data Structures
8:13
Lesson
79
Bubble Sort Pseudocode | Performance & Time Complexity
7:34
Lesson

Design and Analysis of algorithms (DAA)

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