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).
이 강좌는 알고리즘 전문 분야의 일부입니다.
Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
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About this Course
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다음 전문 분야의 4개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 19시간 필요
권장: 4 weeks of study, 4-8 hours/week
영어
자막: 영어
귀하가 습득할 기술
Data StructureAlgorithmsNp-CompletenessDynamic Programming
학습자 경력 결과
62%
이 강좌를 수료한 후 새로운 경력 시작하기
50%
이 강좌를 통해 확실한 경력상 이점 얻기
12%
급여 인상 또는 승진하기
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 전문 분야의 4개 강좌 중 4번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
중급 단계
완료하는 데 약 19시간 필요
권장: 4 weeks of study, 4-8 hours/week
영어
자막: 영어
제공자:
스탠퍼드 대학교
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.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 3시간 필요
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
완료하는 데 3시간 필요
14개 동영상 (총 151분), 4 readings, 2 quizzes
14개의 동영상
Optimal Substructure10m
The Basic Algorithm I8m
The Basic Algorithm II10m
Detecting Negative Cycles9m
A Space Optimization12m
Internet Routing I [Optional]11m
Internet Routing II [Optional]6m
Problem Definition7m
Optimal Substructure12m
The Floyd-Warshall Algorithm13m
A Reweighting Technique14m
Johnson's Algorithm I11m
Johnson's Algorithm II11m
4개의 읽기 자료
Week 1 Overview10m
Overview, Resources, and Policies10m
Lecture Slides10m
Optional Theory Problems (Week 1)10m
2개 연습문제
Problem Set #110m
Programming Assignment #12m
완료하는 데 3시간 필요
Week 2
NP-complete problems and exact algorithms for them.
완료하는 데 3시간 필요
11개 동영상 (총 122분), 2 readings, 2 quizzes
11개의 동영상
Reductions and Completeness13m
Definition and Interpretation of NP-Completeness I10m
Definition and Interpretation of NP-Completeness II7m
The P vs. NP Question9m
Algorithmic Approaches to NP-Complete Problems12m
The Vertex Cover Problem8m
Smarter Search for Vertex Cover I9m
Smarter Search for Vertex Cover II7m
The Traveling Salesman Problem14m
A Dynamic Programming Algorithm for TSP12m
2개의 읽기 자료
Week 2 Overview10m
Optional Theory Problems (Week 2)10m
2개 연습문제
Problem Set #210m
Programming Assignment #22m
완료하는 데 2시간 필요
Week 3
Approximation algorithms for NP-complete problems.
완료하는 데 2시간 필요
6개 동영상 (총 68분), 1 reading, 2 quizzes
6개의 동영상
Analysis of a Greedy Knapsack Heuristic I7m
Analysis of a Greedy Knapsack Heuristic II9m
A Dynamic Programming Heuristic for Knapsack11m
Knapsack via Dynamic Programming, Revisited10m
Ananysis of Dynamic Programming Heuristic15m
1개의 읽기 자료
Week 3 Overview10m
2개 연습문제
Problem Set #310m
Programming Assignment #32m
완료하는 데 3시간 필요
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
완료하는 데 3시간 필요
11개 동영상 (총 124분), 3 readings, 3 quizzes
11개의 동영상
The Maximum Cut Problem II9m
Principles of Local Search I8m
Principles of Local Search II10m
The 2-SAT Problem14m
Random Walks on a Line16m
Analysis of Papadimitriou's Algorithm14m
Stable Matching [Optional]15m
Matchings, Flows, and Braess's Paradox [Optional]13m
Linear Programming and Beyond [Optional]11m
Epilogue1m
3개의 읽기 자료
Week 4 Overview10m
Optional Theory Problems (Week 4)10m
Info and FAQ for final exam10m
3개 연습문제
Problem Set #410m
Programming Assignment #42m
Final Exam20m
검토
4.8
SHORTEST PATHS REVISITED, NP-COMPLETE PROBLEMS AND WHAT TO DO ABOUT THEM의 최상위 리뷰
AA 제공Feb 27, 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.
AS 제공Aug 22, 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.
ML 제공Nov 12, 2019
very interesting course in Algorithms Specialization.\n\nIt is sufficient to arouse interest in studying advance algorithm.\n\nThank you for your teaching, Professor Tim.
JE 제공Dec 27, 2019
I am very confident in the skills I learned. I have read some books on algorithms but this course makes the application so clear regardless of your programing language.
KA 제공Dec 13, 2019
Great course, great explanation from instructor. I loved how Tim Roughgarden explains algorithms. Course Load was just right for me. Great experience!
JF 제공Jan 13, 2018
I love Tim's excitement for algorithms. He really stands out as a quality teacher in his selection of content, explanations and enthusiasm.
LC 제공May 1, 2018
Fantastic course by a fantastic Teacher, Prof. Roughgarden manages to turn the dry subject of Algorithms into a hot sexy one!!
SA 제공Mar 15, 2020
A really well-crafted specialization. Has something for everyone from a beginner to an advanced Computer Science student.
YW 제공Mar 3, 2018
unlike previous 3 courses\n\nsome of course 4's quiz problems are really difficult and not sure how to approach...
AS 제공Feb 25, 2019
Nice course, a lot of information in short time. I think is need more time than 4 weeks to finish this course.
X 제공Nov 30, 2019
Great course in general, got the chance to dive a little deeper into the world of computer science.
PR 제공Jun 24, 2018
Great one for people with advanced understanding! Others will find it slightly hard to follow!
알고리즘 전문 분야 정보
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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