I really enjoyed the course. The only problem was that I was using python 3+ and the programs were written with python 2+. There are some minor differences but I figured the them easily.

The course is excellent. Great quality of supplied material, teachers and assignments. I rate it on the fact that I was able to follow it and learn quite a lot on a subject that is far from easy, and I'm pleased with the results.

The only issue / recommendation I can point out : The course is not very specific on the needed background. As an example, I took this course being an engineer with a decent understanding of maths, a less-decent understanding of physics (almost no quantum physics background), and experience with python programming: in my case, it required a lot of effort in order to follow some of the chapters (specially the ones focused in quantum physics), and normally always more than the normal 8 hs/week. I recommend the student to have some background on probability theory / functions and python programming so as to follow easier, and expect some difficulty in fully understanding some of the subjects. I still rate it 5 stars.

Engage students with the world of Statistical Mechanics by making hands dirty. One needs to have some basics in Quantum Mechanics or Thermodynamics in order to make sense of what have been done. Not sufficient mathematical proof and intuition could be found in Professor's textbook, although it is good to have it free. The solutions to Newton's packing problem is a kind of surprise. Not sufficient conclusions to problems like with and without boundaries; one-half rules; violation of tabula rasa rules; rejection-free direct sampling to avoid Metropolis Algorithm; simulated annealing. These gaps need to be filled in order to make it more self-sufficient. But still it is a very sincere effort to promote this branch of Physics to the world. It is very transferable to Mathematical Finance and Artificial Intelligence.

i love this course, but most codes were not properly explained and rather difficult to tweak.

Nice but demanding course. No course certficate is available after passing the course...

Prepare yourself for the fact that if you want to really understand all the programs and theory in this course you will need at at least 8 hours per week. But if you cope with all the problems you will get not bad skills in python programming and overall statistical mechanics understanding.

I really like this course also I am only confused by my knowledge in computing because this course is very high rated in sense of detailed explanation and easy to follow through difficult themes.

Great introduction to statistical mechanics and Monte Carlo method! This course combined Monte Carlo method and concepts and phenomena in statistical mechanics natually and deliver me to the field of statistical mechanics. Werner's treatment of quantum statistical mechanics not only introduce path integral to students easily, but also help students to form a clear picture of physical phenomena like Bose-Einstein condensation. I will recommend this class to my classmates.

Really a nice journey on Statistical Mechanics. I've learned a lot of interesting things. The team is humorous and funny. I've never seen this form of a course. The only shame, however, is the lack of fellow students. I often feel lonely as the forum is not active enough. But still this is one of the best physics course I've ever had. Thank you!

Material is very interesting and the courser work provides great opportunities to try out and build different statistical algorithms. You don't need a background in physics but I think it would help to fully grasp the materials.

This course is fantastic. Great way to get very good exposure to the Monte Carlo method for Classical and Quantum systems. I highly recommend this to any scientist interested in many body physics & related problems.

Excellent and enthusiastic lectures and tutorials covering a number of topics. Much of the learning took place in the assignments where the concepts were applied and various points were illustrated.

One of the three best MOOCs I have seen.

This course teaches you the fundamentals of statistical mechanics focusing mostly on the algorithms. There are many fields that borrow ideas from statistical mechanics. This is a great introduction for those who need to understand fundamental algorithms in this field but do not have a background in Physics. There was a lot of effort put in preparing the courses. The videos are top-notch, the homework are deep and the textbook provides a lot of necessary background. Although I completed the whole course successfully, the section on quantum mechanics would benefit from a good introduction of quantum mechanics for the uninitiated. I only implemented the algorithms and developed an understanding of the statistics behind them, but I did not quite follow much else. Second, I missed a more direct and formal discussion of temperature and entropy which are major concepts in this field but are never formally introduced in the same way as it is in other courses in this field. Finally, although the homework were illuminating, there was a bit too much homework and peer-correction. It would be better if they could consolidated the assignments into 5 homework every other week and we could do only 2 peer-corrections instead of 3.

This is a graduate or advanced undergraduate level class on statistical physics, focusing on the computational tools (MC and MD). The materials are organized very well and the concepts are illustrated in a clear way. A lot of Python examples are provided to help students master the contents. The homework and exam is not hard, as most of the code is already present by the teachers, and students only need to fill the blank or do a little changes. It's not difficult to go through this course and pass the exam, but it's truly difficult to deeply understand all the materials. Although, for the guys who love statistical mechanics, this course deserve your effects.

It is different from a typical StatMech class that one would learn in a chemistry department. The emphasis on computation and sampling algorithms helped me learn how to do StatMech in practice. The classes are well taught and the homework problems are carefully designed. The programming part of the homework is quite friendly to students who have limited experience on coding, through which they can catch up with the guidance and gain very useful skills. There are also questions regarding the physical insights gained after running the programs and analyzing the data, which are particularly useful for one to achieve deeper understanding of the physics.

This is a really great course! The concepts proposed here are kind of advanced for non physicists (and a full understanding of all the theory beyod it would require much more than 2 and a half months!), but the course is so well managed and the lecturers are so good that I think that most graduate people from semi-thechnical fields can keep up and be very satisfied about everything!

Very clear and very interesting! The exercises are a bit difficult (especially for me that I'm only a beginner in Python) but it's a powerful introduction to computational condensed matter physics!

I suggest it for people that already has the rudiments of Mechanics, Statistical Mechanics and algorithmic approach to every-day problems

You will learn not only the theory about how to solve differente many body problems, but you will laso will adquire the hability to ptogram the solutions for any incoming value in almost any related problem

the best statiscal mechanics course i've taken in my whole live. I also bought the book by Werner, it's very well written

I very interesting course, the course materials are challenging and is by no means an easy course. But in the end it is very rewarding when you understand them. You will understand statistical mechanics from a intriguing point of view. A must take course, if you want to take your physics and computations to another level.

It helps deepen my understanding about Mont Carlo. I had a lot of fun in programing and reading codes or opinions from other students. Our lovely teachers are humorous. They even prepared a big Party at the end of this course XD. hf gl

The video explanations seems very intimidating, but the tutorial and well designed exercises helps to understand the concepts properly. Very well designed course with well thought out syllabus and nice demonstrations.

Wonderful course! Professor Krauth, Michael, Alberto, and Vivien present the material clearly and with obvious enthusiasm for the subject. I learned a ton of physical concepts, Python tools, and applications.

Best course that I've ever taken. Its worth the time and effort. You start from 0 and still you grasp this subject so easily. I'll repeatedly watch the lectures, everything is explained with such depth.

This is a really good course for the introduction of computational methods in statistical physics. Quite a few topics are covered and very subtle and efficient algorithms are developed and discussed.