关于此课程： The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search).
此课程适用人群： 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.
制作方： 斯坦福大学
教学方： Tim Roughgarden, Professor
Computer Science
| 基本信息 | 课程 4（共 4 门，Algorithms Specialization ） |
| 级别 | 中级 |
| 承诺学习时间 | 4 weeks of study, 4-8 hours/week |
| 语言 | English |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
授课大纲
第 1 周
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
14 个视频, 4 个阅读材料
- Reading: Week 1 Overview
- Reading: Overview, Resources, and Policies
- Reading: Lecture Slides
- 视频: Optimal Substructure
- 视频: The Basic Algorithm I
- 视频: The Basic Algorithm II
- 视频: Detecting Negative Cycles
- 视频: A Space Optimization
- 视频: Internet Routing I [Optional]
- 视频: Internet Routing II [Optional]
- 视频: Problem Definition
- 视频: Optimal Substructure
- 视频: The Floyd-Warshall Algorithm
- 视频: A Reweighting Technique
- 视频: Johnson's Algorithm I
- 视频: Johnson's Algorithm II
- Reading: Optional Theory Problems (Week 1)
已评分: Problem Set #1
已评分: Programming Assignment #1
第 2 周
Week 2
NP-complete problems and exact algorithms for them.
11 个视频, 2 个阅读材料
- Reading: Week 2 Overview
- 视频: Reductions and Completeness
- 视频: Definition and Interpretation of NP-Completeness I
- 视频: Definition and Interpretation of NP-Completeness II
- 视频: The P vs. NP Question
- 视频: Algorithmic Approaches to NP-Complete Problems
- 视频: The Vertex Cover Problem
- 视频: Smarter Search for Vertex Cover I
- 视频: Smarter Search for Vertex Cover II
- 视频: The Traveling Salesman Problem
- 视频: A Dynamic Programming Algorithm for TSP
- Reading: Optional Theory Problems (Week 2)
已评分: Problem Set #2
已评分: Programming Assignment #2
第 3 周
Week 3
Approximation algorithms for NP-complete problems.
6 个视频, 1 个阅读材料
- Reading: Week 3 Overview
- 视频: Analysis of a Greedy Knapsack Heuristic I
- 视频: Analysis of a Greedy Knapsack Heuristic II
- 视频: A Dynamic Programming Heuristic for Knapsack
- 视频: Knapsack via Dynamic Programming, Revisited
- 视频: Ananysis of Dynamic Programming Heuristic
已评分: Problem Set #3
已评分: Programming Assignment #3
第 4 周
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
11 个视频, 3 个阅读材料
- Reading: Week 4 Overview
- 视频: The Maximum Cut Problem II
- 视频: Principles of Local Search I
- 视频: Principles of Local Search II
- 视频: The 2-SAT Problem
- 视频: Random Walks on a Line
- 视频: Analysis of Papadimitriou's Algorithm
- 视频: Stable Matching [Optional]
- 视频: Matchings, Flows, and Braess's Paradox [Optional]
- 视频: Linear Programming and Beyond [Optional]
- 视频: Epilogue
- Reading: Optional Theory Problems (Week 4)
- Reading: Info and FAQ for final exam
已评分: Problem Set #4
已评分: Programming Assignment #4
已评分: Final Exam
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制作方
斯坦福大学
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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| 访问课程材料 | 可用 |
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| 收到最终成绩 | 可用 |
| 获得可共享的课程证书 | 可用 |
评分和审阅
Really happy to finish this specialization and I will still study algorithms in the future.
Excellent course! Bravo to the teacher for the commitment provided in this course. Kind regards.
A very nice introduction to algorithms from a high-level mathematical perspective. This course is great for building intuition about key concepts. Highly recommended. I did the Princeton algorithms course first. Both are great. The first tests one more on the implementation side and requires Java. This course is more focused on theory and a great complement. Too bad there is not a Part 5, 6, ….
This is the most challenging course in this specialization. Assignments as well as test questions require good amount of thinking.
One of the best courses I did on Coursera.