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).
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
14 个视频 （总计 151 分钟）, 4 个阅读材料, 2 个测验
14 个视频
Optimal Substructure10分钟
The Basic Algorithm I8分钟
The Basic Algorithm II10分钟
Detecting Negative Cycles9分钟
A Space Optimization12分钟
Internet Routing I [Optional]11分钟
Internet Routing II [Optional]6分钟
Problem Definition7分钟
Optimal Substructure12分钟
The Floyd-Warshall Algorithm13分钟
A Reweighting Technique14分钟
Johnson's Algorithm I11分钟
Johnson's Algorithm II11分钟
4 个阅读材料
Week 1 Overview10分钟
Overview, Resources, and Policies10分钟
Lecture Slides10分钟
Optional Theory Problems (Week 1)10分钟
2 个练习
Problem Set #110分钟
Programming Assignment #12分钟
周
2完成时间为 3 小时
Week 2
NP-complete problems and exact algorithms for them.
11 个视频 （总计 122 分钟）, 2 个阅读材料, 2 个测验
11 个视频
Reductions and Completeness13分钟
Definition and Interpretation of NP-Completeness I10分钟
Definition and Interpretation of NP-Completeness II7分钟
The P vs. NP Question9分钟
Algorithmic Approaches to NP-Complete Problems12分钟
The Vertex Cover Problem8分钟
Smarter Search for Vertex Cover I9分钟
Smarter Search for Vertex Cover II7分钟
The Traveling Salesman Problem14分钟
A Dynamic Programming Algorithm for TSP12分钟
2 个阅读材料
Week 2 Overview10分钟
Optional Theory Problems (Week 2)10分钟
2 个练习
Problem Set #210分钟
Programming Assignment #22分钟
周
3完成时间为 2 小时
Week 3
Approximation algorithms for NP-complete problems.
6 个视频 （总计 68 分钟）, 1 个阅读材料, 2 个测验
6 个视频
Analysis of a Greedy Knapsack Heuristic I7分钟
Analysis of a Greedy Knapsack Heuristic II9分钟
A Dynamic Programming Heuristic for Knapsack11分钟
Knapsack via Dynamic Programming, Revisited10分钟
Ananysis of Dynamic Programming Heuristic15分钟
1 个阅读材料
Week 3 Overview10分钟
2 个练习
Problem Set #310分钟
Programming Assignment #32分钟
周
4完成时间为 3 小时
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
11 个视频 （总计 124 分钟）, 3 个阅读材料, 3 个测验
11 个视频
The Maximum Cut Problem II9分钟
Principles of Local Search I8分钟
Principles of Local Search II10分钟
The 2-SAT Problem14分钟
Random Walks on a Line16分钟
Analysis of Papadimitriou's Algorithm14分钟
Stable Matching [Optional]15分钟
Matchings, Flows, and Braess's Paradox [Optional]13分钟
Linear Programming and Beyond [Optional]11分钟
Epilogue1分钟
3 个阅读材料
Week 4 Overview10分钟
Optional Theory Problems (Week 4)10分钟
Info and FAQ for final exam10分钟
3 个练习
Problem Set #410分钟
Programming Assignment #42分钟
Final Exam20分钟
4.8
51 个审阅71%
完成这些课程后已开始新的职业生涯
67%
通过此课程获得实实在在的工作福利
热门审阅
创建者 AA•Feb 28th 2017
This challenging course improves understanding of algorithms and is intellectually stimulating. I learnt the theory behind algorithms and how they are applied to solve real world problems.
创建者 AS•Aug 23rd 2018
This is the most challenging course in this specialization. Assignments as well as test questions require good amount of thinking.\n\nOne of the best courses I did on Coursera.
讲师
Tim Roughgarden
ProfessorComputer Science
关于 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.
关于 算法 专项课程
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
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