algorithms

About this repository

• Algorithms | Coursera のログ

Contents

Course 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms

• Week 1
  • Part 1: Introduction
  • Part 2: Asymptotic Analysis
• Week 2
  • Part 3: Divide & Conquer Algorithms
  • Part 4: The Master Method
• Week 3
  • Part 5: Quicksort Algorithm
  • Part 6: Quicksort Analysis
  • Part 7: Probability Review
• Week 4
  • Part 8: Linear-Time Selection
  • Part 9: Graphs and The Contraction Algorithm

Course 2: Graph Search, Shortest Paths, and Data Structures

• Week 1
  • Part 10: Graph Search and Connectivity
• Week 2
  • Part 11: Dijkstra's Shortest-Path Algorithm
• Week 3
  • Part 12: Heaps
  • Part 13: Balanced Binary Search Trees
• Week 4
  • Part 14: Hashing - The Basics
  • Part 15: Universal Hashing
  • Part 16: Bloom Filters

Course 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

• Week 1
  • Part 17: Two Motivating Applications
  • Part 18: Introduction to Greedy Algorithms
  • Part 19: A Scheduling Application
  • Part 20: Prim's Minimum Spanning Tree Algorithm
• Week 2
  • Part 21: Kruskal's Minimum Spanning Tree Algorithm
  • Part 22: Clustering
  • Part 23: Advanced Union-Find
• Week 3
  • Part 24: Huffman Codes
  • Part 25: Introduction to Dynamic Programming
• Week 4
  • Part 26: The Knapsack Problem
  • Part 27: Sequence Alignment
  • Part 28: Optimal Binary Search Trees

Course 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them

• Week 1
  • Part 29: The Bellman-Ford Algorithm
  • Part 30: All-Pairs Shortest Paths
• Week 2
  • Part 31: NP-Complete Problems
  • Part 32: Faster Exact Algorithms for NP-Complete Problems
• Week 3
  • Part 33: Approximation Algorithms for NP-Complete Problems
• Week 4
  • Part 34: Local Search Algorithms
  • Part 35: The Wider World of Algorithms