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).
Offered By
Algorithms SpecializationStanford UniversityAbout this Course
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Learner Career Outcomes
62%
started a new career after completing these courses
50%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 14 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish
Skills you will gain
Data StructureAlgorithmsNp-CompletenessDynamic Programming
Learner Career Outcomes
62%
started a new career after completing these courses
50%
got a tangible career benefit from this course
12%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 14 hours to complete
English
Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish
Offered by
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 - What you will learn from this course
4 hours to complete
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
4 hours to complete
14 videos (Total 151 min), 4 readings, 2 quizzes
14 videos
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
4 readings
Week 1 Overview10m
Overview, Resources, and Policies10m
Lecture Slides10m
Optional Theory Problems (Week 1)10m
2 practice exercises
Problem Set #130m
Programming Assignment #130m
3 hours to complete
Week 2
NP-complete problems and exact algorithms for them.
3 hours to complete
11 videos (Total 122 min), 2 readings, 2 quizzes
11 videos
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
2 readings
Week 2 Overview10m
Optional Theory Problems (Week 2)10m
2 practice exercises
Problem Set #230m
Programming Assignment #230m
2 hours to complete
Week 3
Approximation algorithms for NP-complete problems.
2 hours to complete
6 videos (Total 68 min), 1 reading, 2 quizzes
6 videos
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
1 reading
Week 3 Overview10m
2 practice exercises
Problem Set #330m
Programming Assignment #330m
4 hours to complete
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
4 hours to complete
11 videos (Total 124 min), 3 readings, 3 quizzes
11 videos
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
3 readings
Week 4 Overview10m
Optional Theory Problems (Week 4)10m
Info and FAQ for final exam10m
3 practice exercises
Problem Set #430m
Programming Assignment #430m
Final Exam30m
Reviews
TOP REVIEWS FROM SHORTEST PATHS REVISITED, NP-COMPLETE PROBLEMS AND WHAT TO DO ABOUT THEM
by AAFeb 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.
by ASAug 22, 2018
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.
by MLNov 12, 2019
very interesting course in Algorithms Specialization. It is sufficient to arouse interest in studying advance algorithm.\n\nThank you for your teaching, Professor Tim.
by JEDec 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.
About the Algorithms Specialization
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.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.
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