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Why Algorithms are So Important | Algorithm Analysis
(9:00)
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Time Complexity
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Time complexity analysis - How to calculate running time?
14:00
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Approximating Time Complexity | Idea Behind Asymptotic Analysis
12:00
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What is Asymptotic Analysis | Need of Asymptotic Analysis
12:00
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Asymptotically Unequal Functions | Asymptotic Analysis
5:00
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Comparing Functions | Asymptotic Analysis
9:00
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Basic maths needed for time complexity calculations
(8 pages)
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LIVE 1: Basic of Mathematics
69:00
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Asymptotic comparison
(2 pages)
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EXTRA | LIVE 2: Asymptotic comparison of functions
55:00
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Asymptotic comparison tricks
(1 pages)
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EXTRA | LIVE 3: Homework Solution Asymptotic Comparison
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Big O Notation | Asymptotic Analysis
17:00
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Examples on Big O Notation | Asymptotic Analysis
4:00
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Big Omega Notation | Asymptotic Notations
10:00
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Theta Notation | Asymptotic Notations
9:00
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Little oh and Little Omega | Asymptotic Notations
12:00
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Transitivity Property | Asymptotic Notations
5:00
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Reflexivity Property | Asymptotic Notations
3:00
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Symmetry Property | Asymptotic Notations
4:00
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Transpose Symmetry Property | Asymptotic Notations
4:00
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Trichotomy Property | Asymptotic Notations
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EXTRA | Live Lecture 4 : Asymptotic Notation MCQ's
75:00
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Cormen Complexity Part 1
(8 pages)
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EXTRA | CORMEN BOOK READING PART 1
99:00
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EXTRA | CORMEN BOOK READING PART 2
50:00
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EXTRA | LIVE LECTURE: Problem Solving
105:00
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Live Test 1
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TEST 1 SOLUTION DISCUSSION
96:00
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ASSIGNMENT TEST 2
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ASSIGNMENT DISCUSSION
96:00
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Time Complexity: SET 1 (ITERATIVE PROGRAM)
15:00
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Time Complexity: SET 2(ITERATIVE PROGRAM)
15:00
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Time Complexity: SET 3(ITERATIVE PROGRAM)
11:00
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Time Complexity: SET 4(ITERATIVE PROGRAM)
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Time Complexity: SET 5(ITERATIVE PROGRAM)
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Live Lecture : Iterative Program Complexity Examples
72:00
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Recurrences
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Introduction To Time Complexity of Recursive Program
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Substitution Method Examples
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Introduction To Tree Method
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Examples on Tree Method
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Master Theorem For Decreasing Functions
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Original Master Theorem For Dividing Function
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Extended Master Theorem For Dividing Function
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Master-Theorem PDF
(7 pages)
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Greedy Method
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Types of Solution: Solution Space | Feasible Solution | Optimal Solution
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Introduction To Greedy Method
16:00
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Fractional Knapsack Problem
16:00
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Live Lecture: Fractional Knapsack
88:00
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Time Complexity of Fractional Knapsack
13:00
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Job Sequencing with Deadline
12:00
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Huffman Coding
18:00
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Live Lecture: Huffman Coding & Job Sequencing
75:00
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Optimal Merge Pattern
20:00
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Live Lecture : Properties of Spanning Tree
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Application of Spanning Tree
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Counting Possible Spanning Tree
12:00
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Kruskal's Algorithm
14:00
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Prim's Algorithm
14:00
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Live Lecture 1: MCQ's on Minimum Spanning Tree
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Live Lecture 2: MCQ's on Minimum Spanning Tree
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Test on Greedy Method
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Live Lecture 3: MCQ's on Minimum Spanning Tree
74:00
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Live Lecture 4: MCQ's on Minimum Spanning Tree
103:00
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Fractional knapsack. Huffman coding & Optimal merge pattern
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Minimum Spanning Tree
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Divide and Conquer
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Introduction To Divide & Conquer
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Dynamic Programming
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Introduction to Dynamic Programming
28:00
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Single Source Shortest Path: Dijkstra's Algorithm (Greedy Approach)
26:00
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Bellman Ford Algorithm
30:00
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Floyd Warshall All Pair Shortest Path Algorithm
27:00
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Numerical on Dynamic Programming
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0/1 Knapsack Problem using Top Down and Bottom up approach
43:00
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Longest Common Subsequence Problem
53:00
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Sum of Subset
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Backtracking
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Introduction To Backtracking
12:00
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Sum of Subset Using Backtracking
28:00
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Graph Coloring Using Backtracking
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N Queens Problem Using Backtracking
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Branch and bound
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String Matching Algorithms
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Naïve(Brute Force) String Matching Algorithm
20:00
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KMP Algorithm
51:00
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Rabin Karp Algorithm
20:00
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Writing Strategy for Algorithm Paper | Mumbai University Exam
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