I love how this course delves into a very promising advanced research topic in Computational Complexity. It helps me a lot to understand trending publications in the area. Specially that the material is presented in an incremental approach. This way a researcher can live through the evolution of ideas. It is inspiring also in the way one comes up with a partial solution for the special case, and then generalizes with approximation factor that is satisfyingly good. I'm looking forward to more advanced courses in parameterized, streaming or quantum algorithms.

I am a researcher and (in past) an instructor in SDP, Randomized and Approximation Algorithms.

There are a few instances, where things are not explained as well as an advanced UG or a starting Grad student would like, e.g., Knapsack got a bit delirious somewhere in between (the "special special" case, which IMHO was not needed.)

Otherwise, I love Claire's enthusiasm, and the joy she finds in delivering the ideas. She is succinct everywhere (to me).

The assignments could be a bit improved (some are less good, I would personally complain about knapsack), but in general it is a great course, as it gives an accessible introduction to approximation algorithms (for NP-hard problems), which is a very relevant topic, as NP-hard problems are everywhere.

At the time of writing (end summer 2016), it is also a unique course for this very relevant topic.

The course deals not with programming, but rather with designing and analyzing approximation algorithms. The course is on a high level and focuses on the subject without too much side remarks on applications, connections to other subjects or motivational introductions. If you are a theory person, however, you will probably enjoyed it.

Really good course, the material is quite advanced but very well structured and introduced in a very simple way. The assignments were a lot of fun: really enjoyed the peer-graded assignments where I needed to write short proofs, much more useful than regular multiple-choice answers. Looking forward to the next course!

This class is the one I am seeking for a long time. Theories of Combinatorial optimization and associated approximation algorithms involve lots of hot research topics in machine learning, image processing, and Bioinformatics. The faculty for this course is a leading expert in the related fields.

The course provides a high-level introduction to approximation algorithm. There is no programming assignments but it provides nice introduction to approximation algorithm.

Excellent advanced course! Not for beginner in computer science, nor for people more interested in applying computer science than in theoretical foundations.

This course is awesome. Prof. managed to elaborate the problem and analysis clearly and homework is properly assigned.

This was a relatively easy but well paced introduction to approximation algorithms. I totally enjoyed it.

A useful course which introduces key ideas in Approximation Algorithms. Looking forward to part II.

This class is very clear and easy to understand! Thank you for providing such feast for students!

Excellent Course! I have learnt a lot about Approximation Algorithms in a short span of time.

awesome course!

I'd like to see part 2 and other graduate-level algorithms courses on coursera.

A really good course for programmers who want to take a bit deeper into CS.

Very high-level course. After week1 and I'm already excited to start it

Great class, and Professor Claire Mathieu is doing an excellent job!

very nice course. i look forward to the second part!

good course, with many examples and explanations

Really good course and Professor.

I love this course!

Great course!

Very good !

super cool!

Excellent!