So the latest Coursera course I’ve been taking and have now completed is Stanford University’s version of its Algorithms class by professor Tim Roughgarden. Apparently enough people asked about the differences between this course and the one from Princeton University that the question has now been addressed in the FAQ sections of both courses: the Princeton class focuses on implementation and practical programming with students in their first and second university years as the target audience. This Stanford version focuses on the analysis of algorithms and is usually taken by students in their third and fourth years.

I always knew going on that with a weak maths background, this course was going to be hard for me. It also didn’t help that I find myself much more interested in practical applications of algorithms than in studying and devising proofs of an algorithm’s correctness or performance claims. As such I didn’t put as much effort into this as I did for the Princeton course. Some observations:

• The programming homework emphasizes actual implementations, with some tweaks, of the algorithms being studied. It’s a language agnostic course so you’re free to use whatever language you want. The professor just explains everything in pseudo-code while the homework provides a data set and you just have to type in the answers you get from processing the data for evaluation. I cheated. Having already covered much of the same ground in the Princeton course, which does provide complete implementations of all algorithms in Java, I simply used the Princeton library, tweaking code only as necessary to get the correct answers. For the most part, this made the programming homework very easy for me.
• Th quiz portion was correspondingly harder for me. I found that I usually had a decent intuitive grasp of what to shoot for, but in many cases, lacked the discipline to thoroughly walk through the entire reasoning process. Especially since many of proofs requires a solid understanding of probability. The same goes for the final examination, which honestly speaking is slightly easier than the regular quizzes, but is timed and allows only a single attempt. We were allowed two attempts for each of the quizzes. I scored 32 out of 40 in the final exam. I did not attempt any of the optional and ungraded challenge questions.
• I have to say that Professor Roughgarden’s lecture videos and study materials are very basic compared to the Princeton course. It’s just a talking head and slides, with no animations whatsoever. One major problem I had is that the audio frequently is not in sync with what is happening on the screen. For example, he would talk about something but when he refers to a mathematical formula or a snippet of code, it doesn’t show up on the whiteboard until a few seconds later, which certainly does not help comprehension. Also while he offers typed versions of the slides he uses as a download, he prefers the handwritten versions in the lecture videos, and his handwriting isn’t always perfectly legible.
• I did appreciate the thoroughness with which he covers the proofs and I have to admit that I’m able to follow them much more closely than I did when professor Sedgewick went through them (which he didn’t always do in the Princeton courses. I still can’t say that it’s my favorite subject though.
• As previously mentioned, the course does cover much of the same material so there was little here that I haven’t already seen before. I did appreciate learning about bloom filters. And after being frankly puzzled by the Kosaraju-Sharir algorithm in the Princeton course (easy to code, hard to intuitively understand) I really liked how professor Roughgarden’s explanations and the associated programming homework, allowed me to finally grasp it in my head.
• I don’t believe that I ever saw any Community TAs in this course. By and large, as far as I could tell, the students did an excellent job of posing and answering questions themselves, with professor Roughgarden himself intervening only on rare occasions mostly related to technical problems with the course itself. I do find that the student body for this course appears more mature than that of the Princeton course, with few people posting frivolous questions or appearing to be completely clueless.
• Certainly as the professor admits, the assessment system is currently the weakest part of the course. The course really needs a way to have students submit proofs and get them evaluated (which would have utterly defeated me I believe) but there’s simply no feasible way to scale anything like this. Also, the programming homework requires just an answer (usually just a string of numbers) without any of the performance and memory usage evaluations used in the Princeton course.

No surprise when I say that I prefer the Princeton course by a very large margin. But this is still an excellent, high quality course especially for those more interested in the analysis side of things. Incidentally I’ve been invited once again to serve as Community TA for the next offering of the Princeton course which should begin at the end of August.

I’ve been remiss in updating due to being busy on the Coursera site. I’m currently taking the Programming Languages course from Dan Grossman of the University of Washington. It has a strong focus on functional programming, a paradigm that is completely new to me. It’s pretty amazing to learn about these powerful programming idioms that don’t yet exist in mainstream languages.

Meanwhile I am also serving as a Community TA for the current run of Princeton’s Algorithms 1 course. Apparently I got the invitation due to good grades and being active in the forums during the first run of the course. It basically means playing the role of moderator on the official forums and helping students with explanations and basic problem solving when appropriate. It’s sometimes disheartening to note how many students don’t understand basic instructions, for example, like zipping two files is not the same as putting the two files in a folder and zipping that folder. But it’s also great to see students work on the same problems that I spent time on last year, sometimes with results that surprise me.

For those curious, we get moderator powers and can view some personal details of students. We also gain access to a TA-only forum in which instructors and TAs from all of the courses on the platform can interact. We also regularly interact with the course instructor by e-mail. But we have no special behind-the-scenes access to the nuts-and-bolts of the system. So fixing stuff like broken autograders, server outages and the like are done only by Coursera engineers.

Finally if you’re not yet into the MOOC scene, here’s a recent post on the official Coursera blog announcing a huge expansion plan. Those in Asia may be interested in knowing some of the new universities that have signed up include the National Taiwan University and the National University of Singapore. Courses in more languages than just English should be available soon, including some in Chinese. In fact I should be taking a couple of courses on C++ later this year in French simply because they are the only C++ courses on the platform currently available and it is kind of embarrassing not to know the standard language most widely in use in industry.

Updated on 28th December 2014 to fix errors caused by a change in the CodeSkulptor library.

The latest Coursera class I’ve been taking is Introduction to Interactive Programming in Python, a long and unwieldy title. It was conducted by professors Joe Warren, Scott Rixner, John Greiner and Stephen Wong of Rice University and ended yesterday so, as usual, I thought I’d write a few words about it.

To tell the truth, I was misled by the title of the course. I thought it really was an introduction to writing interactive programs. Since all of the programs we worked on in the Algorithms class were non-interactive, I thought this would be a good new experience. It turns out that this course should really be labelled something like Introduction to Programming via Interactive Games in Python, which is even more of a mouthful but more aptly describes the content of the course.

Continue reading End of Introduction to Interactive Programming in Python class →

I thought I should write a note on this course offered by the Ross Business School of the University of Michigan on the Coursera platform that also ended last week. It’s a ten-week course that I enrolled in out of a slight interest I have in the subject, due to my passing knowledge in accounting and economics.

As with Algorithms 1, I learned a great deal but the quality of this course is markedly lower. Most notably, there was a complete lack of instructor presence and oversight on the official forums. This lack of feedback left many students frustrated and encouraged students to freely discuss and share solutions to assignments with each other. With no moderators to rein in behavior, this left the forums a chaotic mess.

The problem was also exacerbated by the limited pool of questions available in the exercises. For these reasons despite that the fact that this course offers a certificate and Algorithms 1 doesn’t, I’d put more stock in the integrity of the grades in the latter. And yes, this even takes into account the fact that the final exam for the Finance course is timed while the one for Algorithms isn’t.

One excuse is that the enrollment for the Finance class was much, much higher than that for Algorithms, so much so that Professor Gautam Kaul might have been discouraged from the start from being able to intervene in any meaningful fashion in the forums. I believe that there were over 50k students in the class. But I also suspect that quality of the forums was also degraded by the fact that many students were motivated primarily by the offer of a certificate rather than the love of learning. I found the many posts in the forums begging the professor to let them have the certificate despite failing the final to be ridiculous and in extremely poor taste.

Finally, while finance is in many ways more practical than algorithms, after all I hold significant investments but don’t make any money from writing programs, I can’t help but feel that it is less relevant. Despite all that I’ve learned, I still can’t see how the models apply to the real world, given that the models are supposed to work only in an ideal environment (perfect market, frictionless transactions, no taxes etc.) It’s all very interesting in a theoretical sense, but I can’t help feeling that it’s all a bit bunk and that academic finance, at least at this introductory level, is pretty much useless in a real world context. I think I’ll stick to the computer courses from here on out.