Statistical Mechanics: Algorithms and Computations

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7/10 stars
based on  4 reviews
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Statistical Mechanics: Algorithms and Computations

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4432 reviews

Course Description

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.
Reviews 7/10 stars
4 Reviews for Statistical Mechanics: Algorithms and Computations

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10/10 starsCompleted
  • 2 reviews
  • 2 completed
2 years, 4 months ago
I'm near the end of this course and I must say that this is one of the best MOOCs I've ever taken (out of the many I've taken). Physics is rarely taught with a computational approach. Even when there are computer programs involved, its wrapped away in isolated courses like "Computational Physics". Ideally speaking *all* physics courses should have an integral computer programming component. "Statistical Mechanics: Algorithms and Computations" is part of a rare breed of physics courses in which concepts are taught from a programming point of view. Seeing an equation and then seeing that equation actually implemented in a short python program is very gratifying. You need to have taken a few prior physics courses to truly understand this material. But if you're super interested in the subject matter I would recommend you dive into it without worrying about the pre-requisites and then decided for yourself if you can manage. ... I'm near the end of this course and I must say that this is one of the best MOOCs I've ever taken (out of the many I've taken). Physics is rarely taught with a computational approach. Even when there are computer programs involved, its wrapped away in isolated courses like "Computational Physics". Ideally speaking *all* physics courses should have an integral computer programming component. "Statistical Mechanics: Algorithms and Computations" is part of a rare breed of physics courses in which concepts are taught from a programming point of view. Seeing an equation and then seeing that equation actually implemented in a short python program is very gratifying. You need to have taken a few prior physics courses to truly understand this material. But if you're super interested in the subject matter I would recommend you dive into it without worrying about the pre-requisites and then decided for yourself if you can manage. The Homeworks are challengingbut not impossible. But most of all the homeworks are FUN and involve programming. What I found unique about this course was the homeworks are peer graded. Initially I thought this would prove to be a drag and tedious. Its not. You end up learning a lot more by grading other peoples homeworks. It does not take a lot of time as the answer key with instructions is provided by the Prof. I could say a lot more. But I want to end by saying that I feel lucky and privileged to have taken this course.
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8/10 starsCompleted
3 years, 2 months ago
I sent a detailed review above but wanted to add an update. I passed the course and am overall quite happy having gone through it. There are a few things that need to be fixed (see 2 reviews above) but if the staff hears out the students, SMAC has the potential to become a flagship course for coursera!
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6/10 starsCompleted
  • 3 reviews
  • 3 completed
3 years, 4 months ago
I (electronics engineer with > 20 years experience) am nearly finished (80%) with the course and believe that I can give a fair and balanced review of Statistical Mechanics: Algorithms and Computations (Feb2014). Pros: excellent video production consists of lecture and tutorial, each ~20-30 minutes 5-8 very short Python programs provided per video interesting material you learn to Markov walk, which is straightforward. Homeworks are extensions of lecture and build upon themselves HWs are unlimited submissions Python2 lang used (not 3); 1st section of HWs sometimes devoted to new usage instructor provided solutions grading is 50% HW and 50% final exam (Pro? Con?) instructor knows his stuff Cons: no slide materials; no free textbook; just 2 videos per week no in-video quizzes for comprehension you better take copious notes of the lectures, since slides were totally absent since HWs are extensions of videos, you suff... I (electronics engineer with > 20 years experience) am nearly finished (80%) with the course and believe that I can give a fair and balanced review of Statistical Mechanics: Algorithms and Computations (Feb2014). Pros: excellent video production consists of lecture and tutorial, each ~20-30 minutes 5-8 very short Python programs provided per video interesting material you learn to Markov walk, which is straightforward. Homeworks are extensions of lecture and build upon themselves HWs are unlimited submissions Python2 lang used (not 3); 1st section of HWs sometimes devoted to new usage instructor provided solutions grading is 50% HW and 50% final exam (Pro? Con?) instructor knows his stuff Cons: no slide materials; no free textbook; just 2 videos per week no in-video quizzes for comprehension you better take copious notes of the lectures, since slides were totally absent since HWs are extensions of videos, you suffer hugely if you don’t grok things no feedback whatsoever on early homework submissions (not designed for it) HWs are not reviews: you must grok the material (do you always?) HW peer assessments are dreadful: expect 10-20% lower grade, really! peer assessment feedback missing-in-action: lazy and/or unqualified 5-10% of HW questions are vague and open to misinterpretation course is severely misrepresented expect to spend 10-30 hours per week without sufficient background college level mathematical maturity is required one formal quantum mechanics course required deceivingly short programs fit into textbook page, not your brain programs contain absolutely no comments to assist the student! Python source code indentation quite strict; you will suffer w/o experience this course is of the type that requires an ASAP post- lecture Q & A class the discussion forums can be helpful but usually too late to help do you already understand partition functions, trotter decomposition, probability density functions, path integrals, list comprehensions? If so, good! instructor not receptive to constructive criticism in discussion forum Summary: I am being generous for giving it 3 stars. I had hopes of learning much. That was not the case. The recommended workload of 4-6 hours is a joke for the 99%. Best to have had prior exposure to Statistical Mechanics, and certainly QM. HW solutions provided really are trivial modifications of provided programs. Couldn’t do it for the QM stuff however. I was left-behind after 4 weeks, and so were many others (per discussion forum). Many HW sections not even attempted by students. I would recommend augmenting all HWs with a multiple choice review section that can instantly provide feedback before proceeding to the balance of the HW. I wonder if the course difficulty introduced a bias in the peer assessments? Anyway, I hope some of the above CONs get remedied for future sessions, if there are any. Finally, this course is my first “difficult” one. During it, I wondered where I stood in relation to the rest of the students. In university, a curve of scores was usually provided so that everyone could really determine how well they were doing, at least by midterm. I have yet to see such a mechanism at Coursera. It would be very simple, near effortless, to provide these statistics on a homework basis for all to view. Since it is very simple to implement for graded classes, don’t you wonder why it isn’t already available? One conclusion is, if it were provided, you would see more students un-enroll sooner from the more difficult classes, which the instructor(s) and MOOC administrators clearly don’t want to happen. This is a legitimacy issue for MOOCs. UPDATE 6/19/2014. Finally, almost 2 months after the course finished, certificates were provided.  I won't mention what I got :).  Here is a direct quote from the instructor regarding the "achievement": [We are thankful to all of you who followed the course, even without attempting to pass it. We are also very proud of all of you who "passed" the course. You did a great job, and you learned a lot. It took real dedication to reach this level, which corresponds to final year undergraduate, beginning graduate level at ENS or another tough University.] (I underlined for emphasis) One more quote: [ Finally, the > 89% level (15 students out there)........ ] It was rumored that 29000 signed up for the course. I've wondered at times, does the "tough" reputation of a school correspond to the inability to teach the material? Apparently, the course will be offered again February 2015.  I hope my review properly prepares those who choose to sign up.  Good luck!
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6/10 starsCompleted
3 years, 4 months ago
This course was extremely ambitious and I'm glad that I managed to get through most of it. It was one of the hardest classes that I've ever taken! I was under-qualified, as I only did about 1.5 year of physics in university, a long time ago, but one of the fantastic things about MOOCS is that you get a chance to try in the first place, even if you don't have the typical background required. I think it's a good idea to bring this type of computational physics to a wide audience of smart, motivated people. That's really whom this course is aimed at, but the staff needs to help us out a little more. We need notes. We need more nimble feedback. We need a more realistic assessment of the required workload. Most grad students in physics unfamiliar with the material would need more time than was indicated, so you can imagine what effort is required of non-physicists. The class takes time. Even evaluating your peers' work takes t... This course was extremely ambitious and I'm glad that I managed to get through most of it. It was one of the hardest classes that I've ever taken! I was under-qualified, as I only did about 1.5 year of physics in university, a long time ago, but one of the fantastic things about MOOCS is that you get a chance to try in the first place, even if you don't have the typical background required. I think it's a good idea to bring this type of computational physics to a wide audience of smart, motivated people. That's really whom this course is aimed at, but the staff needs to help us out a little more. We need notes. We need more nimble feedback. We need a more realistic assessment of the required workload. Most grad students in physics unfamiliar with the material would need more time than was indicated, so you can imagine what effort is required of non-physicists. The class takes time. Even evaluating your peers' work takes time, if you want to do it carefully. At the beginning of the course there were in-lecture quizzes but there weren't any after the first couple of weeks. That was unfortunate. I truly hope that this class runs again and that the staff takes the student body's input into consideration. The material and the homework was overall pretty awesome and I'm not asking anyone to dumb down the class. As we speak I don't know yet if I've passed because the staff has been missing in action for a month now. Very unfortunate.
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