Coursera / Stanford PGM Class is Open | Hacker News Reader

Coursera / Stanford PGM Class is Open

coursera.org

58 pointscbcase16 comments

Game Theory class too: http://www.game-theory-class.org/

Wow, i watched the first video batch and think it's the best online course so far. Very in-depth, but at the same time incredibly well chosen examples. Initially I was sceptical because the videos seem to be taped lectures rather than the type of screencast I know from mlclass and aiclass, but it's really well done.

There is a notable difference of course between courses like PGM and ML, which go pretty much into details of actual things, while others like AI or crypto will probably naturally stay a bit at the surface, as they are more intended to give an overview. I think they shouldn't be compared therefore. I think the robotics class is a bit behind of it's possibilities compared to PGM or ML, considering that this is definitely not an overview anymore.

PGM-class looks pretty hardcore. Well done Prof. Koller & Coursera team. Glad that Coursera and MITx are not watering down the material for any of their online courses.

This has been my main complaint with the Autonomous Vehicles class ran by Udacity I've done thus far. I don't think 30 minutes of video lecture per week can cover close to what an actual college class can. I ended up having to read ~2 hours on Kalman filters after completing the homework to get to a point where I actually felt like I understood the topic as well as I would in an actual class.

Exactly, Udacity courses have around 50 minutes of lectures per week and 6 weeks of lectures. In total just 5 hours of lectures for the whole duration of the course. I don't believe anything substantial can be taught in such a short duration. On the other hand, most of MITx, and Coursera courses run for 10-14 weeks with 2-3hrs of lectures per week with additional assigned readings for some courses.

I personally had pretty bad experience with Udacity's AI-class last year, IMHO their teaching material is mediocre at best. I don't plan to waste any of my time on their classes specially when there are so many other better options available.

I like the Udacity Robot AI course but I can see that it is dumed down. If the course wheren't I probebly would not have the time to talk it.

I trie do to Udacity Robot AI, Coursera Algo 1 and PGM. Lets see if I can handle that. My math background is much to thin and all the math stuff gives me a hard time.

While I agree with everything you said, I kind of liked the AI-class because it was so short and superficial. I felt it gave me a very high level overview of field without taking up too much of my time.

So while the Coursera courses are better in the sense that they cover the material in much more detail and require that you understand the material much better to do the assignments, they end up being much harder for me to squeeze into my already hectic schedule. So in the end I think there is space for both approaches.

That is precisely why udacity courses are gaining popularity. They give you a false sense of accomplishment when actually the learning is very superficial.

Indeed, but there is nothing inherently wrong with superficial knowledge (as long you realize it is superficial). Before the AI-course I knew absolutely nothing about the field, now I know enough to start matching problem domains to techniques. Obviously I learnt nothing useful about how to actually implement those techniques, but at least I know what words to start googling for.

> They give you a false sense of accomplishment when actually the learning is very superficial.

I disagree. Although I've only taken one so far (search engines), and I knew basic Python before taking the course. I like the small weekly practice sessions because they provide just enough breadth to prompt me to seek more depth, run pydoc, etc.

I find it more interesting to learn the standard library and take away a better understanding in this format, as opposed to reading a book or the documentation straight through.

Interesting. I'd like to ask a few questions for future reference on pedagogy :).

Was there anything aside from the derivation of the update equations for Kalman Filters you felt wasn't covered too well? What did you feel that you got from the book [and which book did you use?] that you felt wasn't covered in class?

I'm taking the class too, and feel like I've picked up some pretty interesting algorithms. What sort of things did you learn from your reading? And what did you read?

The CMU PGM course has slides and 'scribe notes' of each lecture if you find something from the Coursera modules confusing:

http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html

(note the navigation bar doesn't load properly in chrome)

The last few weeks I have been reading the first few chapters of the optional text book to get ready for the class - it all looks good, except that the programming assignments are done in Matlab or Octave. I read over the first assignment, and it will be a stretch to "love" Octave.

The course material looks awesome.

Re Matlab/ Octave:

I am an R to Octave convert. R has some great stuff, especially multi-type tables and a decent OO system for packaging complex analyses, but .... Octave rocks R when you have to come up with complex matrix oriented algorithms -- much, much more straightforward.

Also -- cell arrays are funky and necessary for string manipulation. I suggest not dwelling on their weirdness -- you may grow to like them.

Thanks for the encouragement. I have spent a few hours learning Octave this afternoon, and indeed the matrix operation support is nice.

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