Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
Beschreibung
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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-
Taught by: Tim Roughgarden, Professor
Computer Science
Frequently asked questions
Es wurden noch keine FAQ hinterlegt. Falls Sie Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice. Wir helfen gerne weiter!
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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-
Taught by: Tim Roughgarden, Professor
Computer Science
Chaque cours fonctionne comme un manuel interactif en proposant des vidéos préenregistrées, des quiz et des projets.
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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.Syllabus
WEEK 1
Week 1
Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.
16 videos, 4 readings expand
- Lecture: Week 1 Overview
- Lecture: Overview, Resources, and Policies
- Lecture: Lecture slides
- Video: Application: Internet Routing
- Video: Application: Sequence Alignment
- Video: Introduction to Greedy Algorithms
- Video: Application: Optimal Caching
- Video: Problem Definition
- Video: A Greedy Algorithm
- Video: Correctness Proof - Part I
- Video: Correctness Proof - Part II
- Video: Handling Ties [Advanced - Optional]
- Video: MST Problem Definition
- Video: Prim's MST Algorithm
- Video: Correctness Proof I
- Video: Correctness Proof II
- Video: Proof of Cut Property [Advanced - Optional]
- Video: Fast Implementation I
- Video: Fast Implementation II
- Lecture: Optional Theory Problems (Week 1)
Graded: Problem Set #1
Graded: Programming Assignment #1
WEEK 2
Week 2
Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).
16 videos, 2 readings expand
- Lecture: Week 2 Overview
- Video: Kruskal's MST Algorithm
- Video: Correctness of Kruskal's Algorithm
- Video: Implementing Kruskal's Algorithm via Union-Find I
- Video: Implementing Kruskal's Algorithm via Union-Find II
- Video: MSTs: State-of-the-Art and Open Questions [Advanced - Optional]
- Video: Application to Clustering
- Video: Correctness of Clustering Algorithm
- Video: Lazy Unions [Advanced - Optional]
- Video: Union-by-Rank [Advanced - Optional]
- Video: Analysis of Union-by-Rank [Advanced - Optional]
- Video: Path Compression [Advanced - Optional]
- Video: Path Compression: The Hopcroft-Ullman Analysis I [Advanced - Optional]
- Video: Path Compression: The Hopcroft-Ullman Analysis II [Advanced - Optional]
- Video: The Ackermann Function [Advanced - Optional]
- Video: Path Compression: Tarjan's Analysis I [Advanced - Optional]
- Video: Path Compression: Tarjan's Analysis II [Advanced - Optional]
- Lecture: Optional Theory Problems (Week 2)
Graded: Problem Set #2
Graded: Programming Assignment #2
WEEK 3
Week 3
Huffman codes; introduction to dynamic programming.
11 videos, 1 reading expand
- Lecture: Week 3 Overview
- Video: Introduction and Motivation
- Video: Problem Definition
- Video: A Greedy Algorithm
- Video: A Complex Example
- Video: Correctness Proof I
- Video: Correctness Proof II
- Video: Introduction: Weighted Independent Sets in Path Graphs
- Video: WIS in Path Graphs: Optimal Substructure
- Video: WIS in Path Graphs: A Linear-Time Algorithm
- Video: WIS in Path Graphs: A Reconstruction Algorithm
- Video: Principles of Dynamic Programming
Graded: Problem Set #3
Graded: Programming Assignment #3
WEEK 4
Week 4
Advanced dynamic programming: the knapsack problem, sequence alignment, and optimal binary search trees.
10 videos, 3 readings expand
- Lecture: Week 4 Overview
- Video: The Knapsack Problem
- Video: A Dynamic Programming Algorithm
- Video: Example [Review - Optional]
- Video: Optimal Substructure
- Video: A Dynamic Programming Algorithm
- Video: Problem Definition
- Video: Optimal Substructure
- Video: Proof of Optimal Substructure
- Video: A Dynamic Programming Algorithm I
- Video: A Dynamic Programming Algorithm II
- Lecture: Optional Theory Problems (Week 4)
- Lecture: Info and FAQ for final exam
Graded: Problem Set #4
Graded: Programming Assignment #4
Graded: Final Exam
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