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Final Thoughts

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Lecture 38- Complexity_ Fixed-Parameter Algorithms

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Lecture 37- Complexity_ Approximation Algorithms

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Lecture 36- Complexity_ P, NP, NP-completeness, Reductions

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Lecture 35- Linear Programming_ LP, reductions, Simplex

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Lecture 34- Incremental Improvement- Matching

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Lecture 33- Incremental Improvement- Max Flow, Min Cut

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Lecture 32- Greedy Algorithms_ Minimum Spanning Tree

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Lecture 31- Dynamic Programming_ All-Pairs Shortest Paths

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Lecture 30- Dynamic Programming_ Advanced DP

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Lecture 27- Randomization- Skip Lists

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Lecture 29- Augmentation_ Range Trees

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Lecture 28- Randomization_ Universal & Perfect Hashing

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Lecture 26_ Randomization_ Matrix Multiply, Quicksort

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Lecture 25- Amortization_ Amortized Analysis

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Lecture 24- Divide & Conquer_ van Emde Boas Trees

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Lecture 23- Divide & Conquer_ FFT

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Lecture 22- Divide & Conquer, Convex Hull, Median Finding

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Lecture 21- Overview, Interval Scheduling

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Lecture 20- Mid-Course Review.mp4

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Lecture 19- Complexity

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Lecture 18- Dynamic Programming, Part 4_ Rods, Subset Sum, Pseudopolynomial

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Lecture 17 - Dynamic Programming Pt 3- APSP, Parens, Piano

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Lecture 16- Dynamic Programming, Part 2_ LCS, LIS, Coins

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Lecture 15- Dynamic Programming, Part 1- SRTBOT, Fib, DAGs, Bowling

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Lecture 14 -APSP and Johnson

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Lecture 13 - Dijkstra's Algorithm

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Lecture 12 - Bellman- Ford

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Lecture 11 - Weighted Shortest Paths

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