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
43,648 recent views
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. 11 hours to complete
English
Subtitles: English
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. 11 hours to complete
English
Subtitles: English
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
3 hours to complete
Week 1
The Bellman-Ford algorithm; all-pairs shortest paths.
3 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 #110m
Programming Assignment #12m
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 #210m
Programming Assignment #22m
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 #310m
Programming Assignment #32m
3 hours to complete
Week 4
Local search algorithms for NP-complete problems; the wider world of algorithms.
3 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 #410m
Programming Assignment #42m
Final Exam20m
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?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
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.
More questions? Visit the Learner Help Center.
