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MIT 6.1220
Design and Analysis of Algorithms
Lecture 1: Greedy and DP
Lecture 2: Divide and Conquer
Lecture 3: DSU and Amortized Analysis
Lecture 4: Online Algorithms and Competitive Analysis
Lecture 5: Randomized Algorithms
Lecture 6: Hash Functions (Part I)
Lecture 7: Hash Functions (Part II)
Lecture 8: Minimum Spanning Trees
Lecture 9: Maximum Flow (Part I)
Lecture 10: Maximum Flow (Part II)
Lecture 11: Linear Programming (Part I)
Lecture 12: Linear Programming (Part II)
Lecture 13: Intractability (Part I)
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Problem Types
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P, NP, coNP, EXP, and R
Lecture 14: Intractability (Part II)
Lecture 15: Approximation Algorithms (Part I)
Lecture 16: Approximation Algorithms (Part II)
Lecture 17: Multiplicative Weights
Lecture 18: Randomized Algorithms
Lecture 19: Streaming Algorithms
Lecture 20: Random Walks (Part I)
Lecture 21: Random Walks (Part II)
Lectures 22–23: Fast Fourier Transform