Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

About this course: The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

Who is this class for: Learners with at least a little bit of programming experience who want to learn the essentials of algorithms. In a University computer science curriculum, this course is typically taken in the third year.

Created by:   Stanford University

Basic Info
Course 3 of 4 in the Algorithms Specialization.
Commitment4 weeks of study, 4-8 hours/week
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.9See what learners said
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm. 

16 videos4 readings
  1. Reading: Week 1 Overview
  2. Reading: Overview, Resources, and Policies
  3. Reading: Lecture slides
  4. Video: Application: Internet Routing
  5. Video: Application: Sequence Alignment
  6. Video: Introduction to Greedy Algorithms
  7. Video: Application: Optimal Caching
  8. Video: Problem Definition
  9. Video: A Greedy Algorithm
  10. Video: Correctness Proof - Part I
  11. Video: Correctness Proof - Part II
  12. Video: Handling Ties [Advanced - Optional]
  13. Video: MST Problem Definition
  14. Video: Prim's MST Algorithm
  15. Video: Correctness Proof I
  16. Video: Correctness Proof II
  17. Video: Proof of Cut Property [Advanced - Optional]
  18. Video: Fast Implementation I
  19. Video: Fast Implementation II
  20. Reading: Optional Theory Problems (Week 1)
Graded: Problem Set #1
Graded: Programming Assignment #1
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).  

16 videos2 readings
  1. Reading: Week 2 Overview
  2. Video: Kruskal's MST Algorithm
  3. Video: Correctness of Kruskal's Algorithm
  4. Video: Implementing Kruskal's Algorithm via Union-Find I
  5. Video: Implementing Kruskal's Algorithm via Union-Find II
  6. Video: MSTs: State-of-the-Art and Open Questions [Advanced - Optional]
  7. Video: Application to Clustering
  8. Video: Correctness of Clustering Algorithm
  9. Video: Lazy Unions [Advanced - Optional]
  10. Video: Union-by-Rank [Advanced - Optional]
  11. Video: Analysis of Union-by-Rank [Advanced - Optional]
  12. Video: Path Compression [Advanced - Optional]
  13. Video: Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]
  14. Video: Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]
  15. Video: The Ackermann Function [Advanced - Optional]
  16. Video: Path Compression: Tarjan's Analysis I [Advanced - Optional]
  17. Video: Path Compression: Tarjan's Analysis II [Advanced - Optional]
  18. Reading: Optional Theory Problems (Week 2)
Graded: Problem Set #2
Graded: Programming Assignment #2
Week 3
Huffman codes; introduction to dynamic programming. 

11 videos1 reading
  1. Reading: Week 3 Overview
  2. Video: Introduction and Motivation
  3. Video: Problem Definition
  4. Video: A Greedy Algorithm
  5. Video: A More Complex Example
  6. Video: Correctness Proof I
  7. Video: Correctness Proof II
  8. Video: Introduction: Weighted Independent Sets in Path Graphs
  9. Video: WIS in Path Graphs: Optimal Substructure
  10. Video: WIS in Path Graphs: A Linear-Time Algorithm
  11. Video: WIS in Path Graphs: A Reconstruction Algorithm
  12. Video: Principles of Dynamic Programming
Graded: Problem Set #3
Graded: Programming Assignment #3
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees. 

10 videos3 readings
  1. Reading: Week 4 Overview
  2. Video: The Knapsack Problem
  3. Video: A Dynamic Programming Algorithm
  4. Video: Example [Review - Optional]
  5. Video: Optimal Substructure
  6. Video: A Dynamic Programming Algorithm
  7. Video: Problem Definition
  8. Video: Optimal Substructure
  9. Video: Proof of Optimal Substructure
  10. Video: A Dynamic Programming Algorithm I
  11. Video: A Dynamic Programming Algorithm II
  12. Reading: Optional Theory Problems (Week 4)
  13. Reading: Info and FAQ for final exam
Graded: Problem Set #4
Graded: Programming Assignment #4
Graded: Final Exam
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Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.

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