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Design and Analysis of Algorithms

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CONTENT
Instructors
Prof. Erik Demaine & Prof. Srini Devadas & Prof. Nancy Lynch
EXPLANATION

Focuses on algorithm design and analysis, providing both theoretical and practical foundations for those who want to develop efficient solutions.

SUBJECTS

Introduction to Design and Analysis of Algorithms

  • Course Description

Course Schedule

  • Curriculum

Course Videos

  • Lecture 1: Overview, Interval Scheduling

  • Lecture 2: Divide & Conquer: Convex Hull, Median Finding

  • Lecture 3: Divide & Conquer: FFT

  • Lecture 4: Divide & Conquer: van Emde Boas Trees

  • Lecture 5: Amortization: Amortized Analysis

  • Lecture 6: Randomization: Matrix Multiply, Quicksort

  • Lecture 7: Randomization: Skip Lists

  • Lecture 8: Randomization: Universal & Perfect Hashing

  • Lecture 9: Augmentation: Range Trees

  • Lecture 10: Dynamic Programming: Advanced DP

  • Lecture 11: Dynamic Programming: All-Pairs Shortest Paths

  • Lecture 12: Greedy Algorithms: Minimum Spanning Tree

  • Lecture 13: Incremental Improvement: Max Flow, Min Cut

  • Lecture 14: Incremental Improvement: Matching

  • Lecture 15: Linear Programming: LP, Reductions, Simplex

  • Lecture 16: Complexity: P, NP, NP-completeness, Reductions

  • Lecture 17: Complexity: Approximation Algorithms

  • Lecture 18: Complexity: Fixed-Parameter Algorithms

  • Lecture 19: Synchronous Distributed Algorithms: Symmetry-Breaking, Shortest-Paths Spanning Trees

  • Lecture 20: Asynchronous Distributed Algorithms: Shortest-Paths Spanning Trees

  • Lecture 21: Cryptography: Hash Functions

  • Lecture 22: Cryptography: Encryption

  • Lecture 23: Cache-Oblivious Algorithms: Medians & Matrices

  • Lecture 24: Cache-Oblivious Algorithms: Searching & Sorting

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