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12 new universities join Coursera (coursera.org)
207 points by vibrunazo on July 17, 2012 | hide | past | web | favorite | 100 comments



Interesting. I'm hearing a bunch of stuff from colleagues at these universities, and it sounds like it may be a bit rough starting up, especially once it gets to the 2nd semester after these initial offerings.

It took most professors completely by surprise, and it sounds like there may not be bottom-up buy-in (was completely a management decision). In particular, at at least one university, profs are now being told that they may be required to teach a course via Coursera, or at least strongly requested to. And, the universities don't want to budget this as actual teaching, so it's just extra work on top of the normal class load: since Coursera courses aren't credit-hours, they don't give teaching credit. It's supposed to count under "service" or "outreach", I guess, the way serving on committees or reviewing papers or doing a CNN interview does. (This part may vary by university.)

Not sure that's a good recipe for high-quality courses via this method. The advantage of the first few courses is that it was a bottom-up decision by professors who wanted to do it, and devoted significant time to do it right, rather than having it assigned to them by management.


Wow, if that's true that's terrible news. The idea that professors would be forced into this.

If it is true however, there is still udacity, which is much more conservative in it's expansion.


I should note that nobody is quite sure what will happen yet, so that may not be the case. A lot of people found out this was even happening the same time HN did! And the agreements were all signed at a very high administrative level (President or Provost level, which is often a political appointment quite out of communication with the day-to-day operations of the university), so it's not clear there is an implementation plan with dean-level or department-head-level administration yet.

We'll see, though. One thing universities are trying to balance is how to get professors to do more "entrepreneurial" kinds of things while staying. As professoring gets to be a little more like a freelance job (bring in your own research funding, teach online courses via a for-profit company, build up a personal "brand"), one question is what keeps the more successful professors at the university, instead of just going independent, the way Sebastian Thrun did. There's a real possibility that some of the launch professors listed in this announcement volunteered precisely because they're contemplating jumping ship to their own online-course startup, and are using this as a route to build name recognition and audience.


Personally, this is VERY exciting for me. The biggest challenge I have seen with traditional universities getting into "distance learning" is that they are very much focused on delivering a full "classroom" experience rather than tailoring the experience to the medium.

Example: many course videos are of a "live" class and are repeated for every semester regardless of the subject. Next to no re-use of course video is seen from semester to semester. The experience is very much one of time/place-shifting a class experience.

Also, the focus is almost NEVER on interactivity. As a distance student, you're VERY much dependent on your professors willingness to read and answer emails or post in forums.

I was very surprised that MITx had an IRC server set up for the EE class that were offering. I nearly fell out of my chair when I saw they had hundreds of people in the channel.

So, from my point of view, the biggest shift we are seeing is a willingness of the brick-and-mortar universities to consider working with a partner that is going to market their professors and university brand to a very wide audience and yet enough of an arms-length away that there is little risk to the "brand" if things go sour. The fact that there are NO for-credit courses is quite conspicuous and you should expect the universities to attempt to maintain an implicit separation between MOOCs (massive open online courses) and their traditional "residential" offerings.

That said, the writing is on the wall: the traditional model doesn't scale and financial pressure is going to push ALL schools towards this model in some fashion or they will simply become irrelevant. As I drive up and down I-95 in the Northeast, one of the things I've noticed is that there are a massive number of traditional schools advertising on billboards. This NEVER used to happen and most universities (IMHO) considered it a sign of desperation to advertise for students as opposed to recruiting.

What we are witnessing is a race developing. Think back to the early days of Yahoo/Altavista/Google. There are going to be some big winners and some also-rans in this fight. I'm betting on it.

Full Disclosure: I earned my bachelor's degree from Harvard via their Extension School and did the majority of my coursework online using their video delivery platform. I graduated in 2009 at 39 after a LONG absence from school to chase my fortune on the Internet. Best decision I ever made.


That said, the writing is on the wall: the traditional model doesn't scale and financial pressure is going to push ALL schools towards this model in some fashion or they will simply become irrelevant.

I don't disagree there are all kinds of problems. But how does this initiative address any of the financial pressures? It does nothing to reduce the cost structure of universities; in fact it increases the cost structure by adding another thing their professors and administrators must do, not instead of but in addition to everything they currently do. And yet, it does not provide any new revenue streams to pay for that. At least, it doesn't unless there is a payment from Coursera to universities as part of this deal that hasn't been announced yet. My guess is that they're hoping to use it as an advertising loss-leader to attract students and prestige. But that would mean doubling down even more on the traditional high-tuition model, because that's what's going to ultimately subsidize the free courses.

Now if they charged for the online courses, I could see that making a difference in the financial picture. Maybe that's the longer-term plan, for-pay online courses with Coursera and the university splitting the proceeds. That would be closer to the Harvard Extension School model you mention.


How much do you pay for Google searches? For Yahoo! email?

The revenue model is going to be something that is roughly similar to the ones we have with any number of "freemium" type services.

My bet is that there is going to be a certification track that complements the online course delivery. I've seen this with industry certifications (JNCIE, CCIE are ones I'm familiar with) and that the cost to get "certified" will be fairly substantial. It'll also require physical presence and some practical demonstration of competence. For math, I'd expect a traditional sit-down exam. For CS, probably some combination of programming exercise and exam. I don't know how this will work for the humanities.

And one other thing considering the cost argument: paying for the production of a video lecture and the hosting of the content for hundreds of thousands is going to be a fraction of the cost of paying even a grad student to give a lecture on-campus.


It reduces the margin cost for each individual student.


How so? You don't get a degree from these online courses.


You don't get a degree now.

Mark my words: in a few years, you'll be able to certify that you passed each course and you'll get some form of certification for passing a sequence of courses. It might not be called "BS in CS from MIT" but it'll be looked at the same way.


What is the general sentiment in the community about endeavors like this? Only anecdotes, but usually when I see an article like this find it's way to Hacker News I'll see a few posts from people describing themselves as part of the academic community discussing the cons of the way these are being rolled out and implemented.


I would say mixed opinions, but considerable skepticism. Some of the skepticism is of how university administration will handle it; there is some suspicion there is no real plan and this is being done for purely "brand" reasons, so the university president can talk about "cutting edge" initiatives. Some of it is of the concept in general. Some of it is that it's a for-profit company, not a nonprofit with clear charitable goals. Worse, it's a for-profit company whose business plan is still unclear, so one doesn't know what their long-term intentions are.

But a lot of people are just undecided, waiting to see how it'll pan out. Probably not a lot of outright enthusiastic professors, except from the relatively small number who are actively looking to either jump to one of these startups themselves (like Sebastian Thrun), or at least co-found one (like Daphne Koller).

The Chronicle of Higher education's occasional guest op-eds are probably a decent representation of the range of opinion. Here are a cautiously positive and a somewhat negative take, respectively:

http://chronicle.com/blogs/next/2012/07/11/moocs-arent-a-pan...

http://chronicle.com/blogs/innovations/whats-the-matter-with...


My understanding (I used to do some work in distance education) is that one of the reasons Harvard/MIT founded eduX rather than partner with Coursera or Udacity is they were unable to get past the 'for profit' model, and there was some unwillingness to discuss how the 'profit' was going to be made.

I think a lot of the academic scepticism does come from the for-profit - not to say for-profit education can't work, just that it is somewhat new territory for many institutions.


I wonder why they couldn't (or didn't choose to) partner with individual professors on a per-course basis instead of entire institutions whose professors might not all be interested in participating. Is it because individual professors might not have complete autonomy to do so without the permission of the university? Or maybe partnering with an entire university ensures a greater and more recurring supply of courses? Or that the university might help incentivize what is surely a very time-consuming task by having a course replace some other service requirement?


Probably all of the above, although I think Udacity was created as a separate entity because the Prof's had some legal issues with Stanford. So having permission is probably pretty important.


Who on HN is taking one or more of these courses?

I signed on for Daphne Koller's Probabilistic Graphical Models and Geoff Hinton's Neural Networks courses. PGM is supposed to be really tough. I am planning to take a couple of months off from work.

This new world, where you can learn for free from the experts, is a dream come true.

I am thinking of blogging my progress (or lack of it, as the case maybe!)


can learn for free from the experts, is a dream come true.

I'm not sure that's what they do though. Learning "from the experts" means they are there to help you, understand where you're coming from, and correct you along the way. Just watching a video of someone with recorded slides is something we could have done 20 years ago.

I was excited about the PGM class, but my interest dropped after the first few videos (100% comic sans slides, poor video quality, poor audio quality).

Just because you are an expert in some field has zero bearing on your ability to teach or convey information. I'm not sure a "let's imitate college lecture formats" site founded by smart people understands that. They fall into the trap of "smart people can do EVERYTHING!" There are a lot of very smart people who have negative presentation skills. There are a lot of very smart people who have excellent presentation skills too.

Solution: have the smart people write the script, but have people excited about presenting show it to the world.


I'm smart, and I prefer it that way. Actually, I wish it was even more concentrated, more expertise, less "teaching".

Ultimately, I believe, there will have to be different (types of) lectures for different people, and different speeds.

The UI, however, could be improved.


Why not just learn from a textbook? It's extremely concentrated, written by an expert, and you don't have to deal with any of that "teaching." Loads of problems to work out too.

I do like the programming interface that these sites use, to work out problems and get instant feedback.

Would you consider yourself an auditory learner? If that's your preferred learning style, I guess I see the value in these recorded lectures.


I signed up for a Coursera class (just started today, actually), but this has been my line of thinking. I find value in attending classes in person, as being there and interacting adds to the learning experience. But an online class? Wouldn't a textbook be just as beneficial?

Anyway, I wanted to see what one was like. Since it's free of charge, no big deal if I don't care for it. But I'm curious.


That's what I've been thinking: why not replace (or supplement) the videos with plain old html with embedded pictures/video/response forms as necessary?

In a Udacity course I took (the driverless car one with Thrun), they did have a (community-generated) html version of the lectures, but you would still have to go back to the videos to get credit for having watched them and answered the questions -- and, of course, it was always a work in progress.

The advantage over textbooks would be in a) hyperlinking, and b) a known group of people doing it at the same time as you so you can discuss the material and get answers from the professor on the most common questions.


The main reason was that the timing of the videos and the deadlines for the exercises actually made me do it. I would probably be less committed to a text book.

Having said that, I prefer learning from notes. Not text books, because they are usually way too verbose, but during my studies, I would usually skip lessons (and save 1.5h on commute), and study using my classmates' notes.


I am so with you on this. I actually bought the PGM book about a year before PGMClass but just stalled out in chapter 5. Without some kind of externally enforced schedule for learning I'm hopeless.


udacity has gone too far the other way for me. It seems very slick but I switched off after a couple of minutes because I felt like I was watching a kids tv show.


I am taking the udacity statistics 101 course and while I agree with the statement you make, if you visit the forums there are plenty of people having trouble with the current material itself. The last assignment had a fill in the blanks proof of the maximum likelihood estimator. Some people were complaining on the forum that it did not belong to a 101 course. Either ways I am glad that the course is being offered. It may not be the best introduction to statistics I get but it at least pushes me into starting to read about statistics.


That wasn't supposed to be a comment on the material, simply the way it's being presented. This was a discrete mathematics course, don't have time to look it up right now. (edit: Relatives are faffing - here it is: http://www.udacity.com/overview/Course/cs221/CourseRev/1)

I personally think there's room for many services like coursera and udacity to serve different levels of academic experience and expectations.


I realise my initial comment might have come across as more confrontational than I intended. I definitely agree that there's room for services catering to different academic experience and exceptions. What I wanted to highlight was even the hand-holding approach of the udacity instructors might be to cater to absolute beginners.


Great to know. I've been working on a third option that's less lecturey and less condecending. People can complain about it profusely next month when it launches.


I watched most of Daphne's videos and had the opposite reaction - I was blown away at how good they were. I felt as though I had a world class expert standing there in front of me explaining things very clearly. I particularly enjoyed that I never felt like things were being "dumbed down". I didn't notice the comic sans - actually maybe I did subconsciously and maybe this even improved the impression on me "how quaint - someone who is so into their work they don't even realize people make fun of you for using that font". There were video and audio issues, but to me they were secondary.


I took the Machine Learning course by Andrew Ng some time ago. And it was a really great experience.

The lessons are interesting, and I really like their concept of quizzes in the middle of the lessons. They just show the question on the video, he explains it, and they stop the video and you can choose which one is right, or type in the number.

The homework is also well done and enhances the knowledge from the course. I had a lot of fun doing the lectures each and every week. You probably want to put aside a set day and time when you will do it, though, or else it is easy to do other things and "do the lectures later".

If they continue like this, there is soon no reason to go to a university anymore. :) (which is a catch-22 since the lecturers are payed by universities...)


Prof. Ng's ML course was a real eye opener. It wasn't hard but was very practical. Then when you need more theoretical background you can watch recordings of his stanford ML class.


I've been watching the lectures for the same course. Having them split into short self-contained videos (~5-15mins) has made it very easy to fit in around university and a toddler.

For example, whenever I do the washing up I put my tablet on the windowsill and watch a video. I've also been watching them instead of browsing the web as a more structured break when studying. The fixed length of a video makes it much easier to be disciplined with the length of my breaks. The subject matter is different enough and presented in such a digestible way that it really is a good break.


I didn't like the homeworks on the ML course. The programming exercises were child's play for someone who already knows how to program, you just had to translate math equations to code. I hardly learned anything by doing them.


You were taking the wrong course, I would argue. You should do the one on AcademicEarth (CS 229). The online course was CS229A Applications of ML.


You thought that figuring out how to vectorize the solutions was "child's play"? Sure, coming up with any-ol' working solution was child's play, but figuring how how to get the solutions to be one or two lines of code containing only matrix operations wasn't child's play. It wasn't rocket science either, but I had to put on my thinking cap a bit.


One thing I've noticed in most of the online classes is a tendency among a vocal subgroup to talk about how easy the material is, even when it's not.

It's just alpha nerding, same as any other venue. And if it's really that easy, then as you say, why not impress us with how you've gone beyond it. Anyone who can type can say it's easy. Show us something.


Why can't we have a real discussion here on HN, without our egos being hurt and without throwing around such subtle insults as "alpha nerding"?!?

I was just being honest. The exercises were easy for me. I'm sure they were not for many people. I don't care, because I don't compare myself to others in this way. In comparison to other people around me, I know I'm very smart/capable for some things, and suck at others. I'm not saying this to feel better or so that you would respect me more. I'm saying this because it's fact. The way I see it (without having to compare myself to other people), I'm capable enough to reach many goals in my life, but I would like to improve myself in many areas still.

But anyhow, you've completely missed my point.


As you say, not child's play, not rocket science. But, that's besides the point. That has nothing to do with programming ML, but everything with programming Matlab. As I said, the programming exercises would be more suitable for a programming course, not a ML course.

In addition, vectorization wasn't even necessary to solve the problems. Yes, that's how I too made the exercises more interesting, but that could be done with almost any kind of programming exercise in Matlab.


I think there's some benefit in figuring out the math well-enough so that you can translate each formula into a small number of linear algebra operations without any loops. They weren't always expressed that way. If they had been, then the homeworks truly would have been child's play.

If it's concentrating more on the math than on applying the stuff to real-world problems that bothered you, then you wouldn't want to take the real full-blown Stanford class. That class is largely doing rather difficult math proofs and the like.

I'm not sure I understand the argument that you could have solved the problems in a lazy fashion, however. Such lazy solutions aren't good for the real-world, as they don't perform well-enough. Part of the insight taught in the class is that you have to look for these sorts of performance optimizations in order for the solution to be feasible to use in the real world.

If the problem is that other students could pass the class without having put in the same effort as you and I did, who cares about that!


I don't want this to develop into a flame war, so this will probably be my last reply in this thread...

I studied math. I love math proofs. It's just not what I would expect if I would sign up for a ML class. Note, however, that I'm not complaining about the contents of the ML class, which I think were awesome, a really nice broad overview of the ML techniques. I really think I learned a lot. I'm just complaining about the homeworks, nothing else. They were not ML homeworks, they were CS homeworks (particularly, Matlab class homeworks). I don't know the contents of the real Stanford class, but believing your description, the same kind complaint applies.

You're wrong when you say that "lazy solutions" don't perform well in the real world. In the real world, robots aren't programmed in Matlab (I hope). In compiled languages, loops are just as fast as vectorized code (which is implemented using loops). Vectorized code having better performance than imperative looping code is just an artifact of the fact that Matlab (and e.g. R) are interpreted languages. (Sure, there might be some additional optimizations possible on vectorized code, like SIMD instructions, cache locality, special algorithms for sparse vectors/matrices, but this is in most situations premature optimization. Being able to write correct code and read it later beats 2% gain in performance!)


The reason for all the math proofs in the real Stanford class is, I assume, because it is training you to have a deep understanding of Machine Learning, and perhaps even to become a Machine Learning researcher. This is the type of education that I received at MIT, and there's much to value in it. If you can do all these proofs, then you probably really understand things. On the other hand, it can be brutally difficult, and I still have stressmares when I sleep to this day over this brutally difficult style of learning.

If you really want to understand ML, you should do the real class. All the material is online, so you could do it at your own pace.

The online class, however, was a watered-down version of this, and designed to give you a taste of the real class without being brutally difficult. Perhaps it will inspire some people to do the work of the full-blown class.

On the other hand, sometimes you just want to know how to apply some technology to real-world problems without having to understand all the gory details. I think the online AI class is more along these lines. They had a final project, for instance, that was structured as a contest to see whose solution worked the best. I think they used canned ML packages instead of coding them on their own, but then wrote their own code to apply the canned ML software to some sort of open problem. Perhaps you would enjoy that class more.

As to getting only a 2% performance gain from vectorizing your code, I'm skeptical of this claim. I know that highly respected people (e.g., Guy Steele) have claimed that people still use Fortran, despite its drawbacks, in part because of it superior performance over C in being able to vectorize code and getting substantial performance gains. Furthermore, a vectorized algorithm is more easily adaptable to a Hadoop cluster, or a GPU, and both of these can lead to huge performance gains.

In higher-level languages, such as Java, vectorized solutions can also lead to much higher performance, as you can use a native linear algebra library, which will, no doubt, perform better than Java loops.

Furthermore, if you can turn an algorithm into linear algebra, you will have a much more maintainable solution, as you can turn a program that is several pages long into one that is several lines long. That is surely going to be easier to maintain!


Many of the computing and math courses are enough in my comfort zone that I can readily pick them up on my own. That, coupled with my belief in the future of quantative biology has made me sign up and take a number of chemistry, anatomy, medicine, drug, neuro and genetics courses. About 13 in all but only 2, no more than 3 per block of time which is good. The only outliers are the quantum computing and finance ones.

I've programm(ed|ing) a spaced repitition native app that uses ideas from online sequential learning to challenge me (hah online learning for online learning). I can look at performance graphs per topic area over time. For now it just scores roughly based on text similarity but I've built a parser I can use for the more mathematical ones. By the end of a couple of courses it should be a very interesting project. It currently additionally allows for searching and note taking but I noticed when I go over my notes I tend to go back and forth on certain concepts so I will implement a history concept and automatic topic map building (using both cookie trails and measures of similarity). An unintentional side effect due to Markdown support and html generation is that it generates something like a wiki but far easier.


If someone came up with a good spaced repition app for retaining the learning from all these online classes, I'd be happy to pay $20 - 30 for something like that (market survey of 1)


Anki is open source, runs on all platforms. And is imho amazing


It's very important to me that I retain the information so I'm testing this on myself. My motivation is not monetary though.

My approach is inspired by http://en.wikipedia.org/wiki/Roger_Craig_(Jeopardy!_contesta...


yeah, I read about that in http://www.amazon.com/Final-Jeopardy-Machine-Quest-Everythin...

I'm not enough of a hacker yet to actually do that myself


I have already taken 5 courses and I was thinking the same.


Hi, do you have an email I could reach you with? Your profile is empty.


I am taking the Introduction to Cryptography course at a leisurely pace right now. Experience has been awesome so far, and am definitely looking forward to taking more courses on the site.

After aimlessly reading HN and blogs for so long, having a solid, structured course feels so... good. I need to mentally prepare myself for this very real thing called "learning" once again!


Great! I've signed up for "Computing for Data Analysis".

Personally, the old saying

Not only is a book the better teacher, it also has more character.

holds mostly true for me. Most classes, that I remember to be superior to the experience of a good book, were either because there simply wasn't a good book, or the teacher was extremely charismatic, or both [1]. However, I really like this movement, see its appeal and learnt a thing or two from these new [2][4], and old [3], online courses.

As others have pointed out here, watching a short video has very little transaction cost. That is true, however, for me the transaction cost with a book is often lower. With a book I can easily navigate to the interesting part. With the online videos, I never know if I can skip over the intro stuff. For instance, in Ng's ML class [4] a lot of the material, especially the majority in the first six chapters was familiar to me. However, had I not watched the first chapter I still wouldn't know the definition of a Hypothesis in Machine Learning. This problem exists with books, too. But I think skimming is easier with books.

[1] Underactuted Robotics http://ocw.mit.edu/courses/electrical-engineering-and-comput... [2] http://youtu.be/0yD3uBshJB0 [3] http://www.udacity.com/view#Course/cs373/CourseRev/apr2012 [4] https://class.coursera.org/ml/lecture/preview#close


And if you encounter a term that you missed by skipping the introduction it's easier, with a book, to quickly find where it's defined courtesy of the index.


Or courtesy of being able to quickly scan and skim the pages. And that's the same reason why videoutorials are inferior to traditional, text-and-screeshots-based tutorials. A point worth keeping in mind IMO.


Completely agree. However, the advantages of the Coursera courses are that they follow a schedule, have quizzes and assignments, and have active forums where you can ask (and answer) questions.

I easy to buy a book on an interesting subject (I do it all the time), but it's much harder to actually read it, especially if it's on a challenging topic.

For me, the best solution would be if the courses had the material available in text form as well. Then you could choose how to learn (video or text - I would choose text).


I have the same problems with books. I think this is because books tend to cover a lot of material. A short course on the other hand can focus in a self contained subset, which you can expand later.


Good point. The Udacity course on "Programming a robotic car" actually provides written pdf's. Unfortunately its hand written.


Right. So maybe, the ideal education material is more what was imagined in the past. An interactive book with quizzes including videos and high-res pictures in the places where it makes sense.


Who on HN is taking one or more of these courses?

I'm trying to get used to the model by taking courses that will mostly be for review (but I expect that they will get into some material that is new for me, or forgotten through lack of practice for me) such as

Statistics One by Princeton University starting 3 September 2012, taught by Andrew Conway

Calculus: Single Variable by University of Pennsylvania 27 August 2012, taught by Robert Ghrist

Introduction to Logic by Stanford University 24 September 2012, taught by Michael Genesereth

I'm currently in the Udacity introductory courses on statistics and physics, again for personal review and also to check them out for my children. I'm intensely curious about how this course delivery model will develop, so I'm jumping in to use the Massive Open Online Courses to study subjects I like to self-study anyhow.


The Intro to Logic course is pretty good IMO. The lectures are pretty dry (not much that you can do about that really), but the content is solid.


I'm taking the cryptography course by Pr. Dan Boneh, it is awesome, it is really engaging, deep enough and all the material is very well done. Moreover I just discovered the second part of this course has been announced and is scheduled for early 2013, so it's fabulous.

All I can say is that for this course I would even be willing to invest myself even more (both in time and money) at the level of requirement of a graduate course. I really would be interested in paying the standart fee and follow the same course than the stanford students follow and have a kind of official credit in reward.


Yep, i liked the crypto course a lot too (took part in the first iteration in March).

The problem is that there is basically no "service" at all. The first iteration was delayed by several weeks (without any prior notice or status update) and there were several problems with the certificates (Ive gotten less programming credits than I should have and others had similar problems) but got no chance to get this corrected.

I guess thats the problem with free courses (and thats the reason I would like to pay some fee for it): You can just record the lessons once and afterwards repeat the course how often you want with very little updates - and very little help/service for students.


I signed up for 'Software Engineering for SaaS' and going to take more programming courses when i have more time... its really amazing, the courses are so much butter than at my university...


"the courses are so much butter than at my university"

That's the real challenge to all the middle-of-the-road schools out there. Now they can be directly compared to the best.


This new world, where you can learn for free from the experts, is a dream come true.

What's the business model though? Because that can only go so long without the teachers/experts earning money.. and if they earn money, someone else must be also earning the money to pay them.


So far it seems to be basically a "subsidized by universities" model. The people giving these lectures are earning traditional salaries in traditional professorships, paid for by a mixture of tuition, research grants, and (for state schools) tax money. Of course, that doesn't explain whether it'll be viable long-term for the universities to keep subsidizing it. My guess is they're hoping to either: 1) use it as a loss-leader that pulls in more traditional, tuition-paying students; and/or 2) use it as a loss-leader that pulls in some kind of other revenue source, e.g. impressing people at companies who are in charge of grant programs; and/or 3) charge at some point.


I assume it was strictly donation at this point. If I were to design something like this (actually not that far off from a nonprofit I had in mind years ago), I would offer a freemium model: basic classes for free, more advanced classes for a fee.


recruiting fees from employers for the best students


Didn't know Hinton is doing a NN course. Hope he gets into the deep net stuff. For those interested too, link is: https://www.coursera.org/course/neuralnets


I took ML Class (quite easy) last fall, PGM Class (a ton of work, but well worth it) in the spring and have just started the Quantum Computing class. Be careful, they're addictive!


I took machine learning the first time it was offered, and took about half of the PGM class before personal commitments won out temporarily (it was quite demanding, 10-15 hours per week). I re-signed up for PGM so I can give it another go this fall. Quality was really fantastic. Some people complained that ML was not as hard as the Stanford class, but I probably wasn't expecting quite that level in a free, online class, so expectations on my end were more reasonable.


I'm taking the cryptologie one, very interesting but also a bit hard to follow. I'm on week 4 whereas week 6 is already on and feel like I won't have time to finish it by the time the exam happens :(

I've tried the sociology one but really didn't dig the first texts we had to read.

I'm waiting for gamification, python, object in french, crypto II, greek mythology...


I signed up in Algorithms I and Functional Programming in Scala.

I'm also taking Algorithms in Udacity, but I don't really like Udacity too much.


I'm doing Farmacology right now.


I've taken two courses from Coursera. This spring I took Design and Anaysis of Algorithms part 1, and last fall I took Introduction to Databases.

Both were great experiences, and I learnt a lot. However, each course was many hours of work per week for me. There are easily 10 courses I would like to take from the current offering, but for me (have a family, working full time), the biggest constraint is time. Nice though to have the choice once you decide to take one.

I have written in more detail about my experience with Coursera: http://henrikwarne.com/2012/05/08/coursera-algorithms-course... and http://henrikwarne.com/2011/12/18/introduction-to-databases-...


I'm going to be in classes for the rest of my life. Literally. I love it.


Me too! :D


Great.

I took a Coursera course last month, and despite a couple of weirdnesses, it was a really enjoyable experience. I don't really get Udacity, but I'm definitely going to take a couple more of these courses.

It also appears as though they're starting to figure out how to mark essays, etc without involving huge numbers of lecturers. It's an exciting time for eduction.


I'm curious to know how are they marking essays. Can you say more about it? Links are fine, too.

EDIT: Oh, nevermind, I've found it. It seems that student grade each other's essays - one student grades three other students.

Look here, under Peer Evaluation: http://spark-public.s3.amazonaws.com/fantasysf/Documents/Wor...


I'm signed up for that particular class and I've been wondering how it was going to go.

The workload looks reasonably significant, which I'm happy about. I'm a little skeptical of peer grading of essays, but it's something they've got to figure out for the future.

Every class I've done so far has had a tiny minority of forum whining about grades. I'm expecting it to be notably higher in this class, but we'll see how it goes...


One thing UDACITY does better is that it is much more structured. They have courses that build on each other.

Coursera seems like bunch of interesting courses put together, rather than structured degree thing.

I think this is because universities see coursera as way increase its reputation with the public rather an alternative to university lectures.


This is great and I'm really excited for the new courses. I'm currently taking Vaccines and Pharmacology from coursera and Statistics from udacity. These courses help me do exactly what no university let me: Learn about interesting topics from different areas. This way I can dive into a topic more deeply if it interests me, and if not I'll just know some basic principles. Jack of all trades, master of none is absolutely fine for me.


Looking forward to Odersky's course Functional Programming Principles in Scala.

https://www.coursera.org/course/progfun


Kinda interesting that SICP is the first book on the suggested reading list. Too bad the start date is months away!


Odersky's own Scala book has some large examples that are based on sections of SICP. E.g., the digital circuit simulator.


SICP should be on the suggested reading list for basically all CS classes :P.


The Coursera website is iterating change faster than it can add new courses. You can feel it going from 0 - 60 with each new visit. OTOH, it's also beginning to feel -- big.

Udacity seems more approachable and familiar. Oddly enough, even with 2000+ episodes, so does Khan Academy.


And whoever programmed the "sort by start date" function of the new course overview page that went live yesterday or today should probably take the introduction to algorithms course :-). It looks like it sorts with a comparision function that returns a falsy value both for "no date < some date" and "some date < no date".


Interesting list. The one surprise is that Caltech choose to go with Coursera rather than the much more open edX.


I see that this course teaches rails https://www.coursera.org/course/saas

There is one that teaches Django?


"Software as a Service" is an architecture paradigm: little of it is Rails or even web specific. In fact, any competent software developer should be able to get the gist of Rails, Django or flavor of the month framework in the proverbial weekend.


Indeed, but the nitty gritty of frameworks can months/years.


The online course doesn't take months/years, so the nitty gritty of Rails is neither taught, nor is it a requisite for the course: in complete agreement with my position.


I wasn't disagreeing...


Its great to see my university (Illinois) finally on this list. We already do record the more popular courses and make them available to students (internally its free, but if you're not a student you have to pay for it). However, it seems like the course that would be most useful (Computer Vision by Derek Hoeim http://www.cs.illinois.edu/~dhoiem/) is not yet on the list. I wonder whether the university will make that one available because when I took it there were a lot of external students (usually sponsored by their companies) and I'm sure the Uni made a lot of money from it


There's a computer vision class offered already: https://www.coursera.org/course/vision.


I can't believe Jitendra Malik himself is teaching this! He is an extraordinary researcher in computer vision, and I would definitely like to attend this. Thanks!


The exciting part of this for me is seeing that some of the intro courses are taught by top senior lecturers rather than just professors. I've always found some of the best learning experiences for material that isn't heavily connected to research comes from people who's careers are in teaching rather than research. One of my knocks on Udacity was the whole PhD required approach to teaching intro CS, many of the best lecturers in first year CS don't have PhD's.


The Gamification course (https://www.coursera.org/course/gamification) seems to be a great resource.

It is one of those topics that there is not too much helpful material online - so I am looking forward to it.


I'm curious - why isn't UC Berkeley mentioned, but this course, taught by a Berkeley professor is listed on the right? https://www.coursera.org/course/qcomp


This is awesome! Signed up for Computational Investing and Dan Ariely's class on Behavioral Economics.


Anyone knows if such course exists for video game development?


Great,that is awesome.




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