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Elite Computer Science Mastery
·
Module 6
Dynamic Programming and Greedy Algorithms
1
Recognizing When to Apply Dynamic Programming
Identify optimal substructure and overlapping subproblems to recognize when to apply Dynamic Programming (DP).
2
Memoization vs. Tabulation: A DP Trade-Off Analysis
Implement top-down (memoization) and bottom-up (tabulation) DP strategies and compare their trade-offs.
3
1D and 2D Dynamic Programming
Solve 1D DP problems (e.g., Fibonacci, Climbing Stairs) and 2D DP problems (e.g., Longest Common Subsequence, Edit Distance).
4
Knapsack and Subset Sum Problems
Apply DP to solve knapsack-family problems (0/1 Knapsack, Subset Sum).
5
Bitmasking for Subset and Permutation DP
Solve advanced DP problems involving subsets or permutations using bitmasking for state compression.
6
Greedy Interval Scheduling
Apply the greedy algorithm strategy to solve the interval scheduling problem and prove its correctness.
7
Huffman Coding: Optimal Prefix Codes with Priority Queues
Implement Huffman coding using a priority queue, explaining the greedy choice that leads to an optimal prefix code.
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Graph Algorithms
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System Design: Scalability, Caching, and Storage