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Shortest Paths Revisited, NP-Complete Problems and What To Do About Them, Université de Stanford

4.8
385 notes
49 avis

À propos de ce cours

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)....

Meilleurs avis

par AA

Feb 28, 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.

par AS

Aug 23, 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.

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47 avis

par Dilyan Marinov

Feb 06, 2019

Very challenging, but worthwhile.

par Anton Berezin

Jan 19, 2019

Tim, thank you very much!

par bangchuan liu

Jan 07, 2019

this course is harder than other 3 courses, but it is very worth to take it.

par Yusuke Urakami

Dec 23, 2018

There were countless challenging and stressful moments during this specialization for me because I didn't have any CS knowledge or experience before. But, all in all, it totally worth it, a lot of learning and aha moments involved while torturing myself. I definitely recommend this course to everyone who want to dip their toes into CS world.

par Thao Nguyen

Dec 15, 2018

Very challenging, but I learnt a lot from the course.

par Liankun Zou

Dec 02, 2018

The Course is challenging and I like it

par Simeon Malchev

Nov 24, 2018

Excellent course, strongly recommended!

par biswa

Nov 11, 2018

Great material!

par Ashish Dattatray Sarode

Aug 23, 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.

par Yiming Zhao

Aug 13, 2018

Really happy to finish this specialization and I will still study algorithms in the future.