Coursera/Stanford online algorithms I course – a retrospective

Difficulty can be a good indicator that you're at the edges of your current understanding.

The MIT electronics class often poses questions during the lectures or in the homework that are based on what you've already covered but involve concepts which are still upcoming.

This is difficult, but it's vastly superior as a learning technique as you have to work your way through it for yourself.

I didn't catch algorithms but am dipping my feet into automata, logic, compilers and machine learning right now. I'm going to have to drop compilers and machine learning because it's too much work for my schedule right now but I will still follow the lectures. It's a mixed bag, I must say. The user experience of the website itself works a charm. The classes themselves, not so much. After being addicted to khan academy for a while and trying udacity, the dryness of the coursera classes feels like a big setback. I am watching the same slide for 2 minutes while a teacher throws a lot of technical lingo at me and the only way it feels like an improvement over a brick and mortar school is that I can rewind until I've figured out what he is trying to say. This is in opposition to other courses out there which do involve you strongly in ways that are more suited to the medium. I have no complaints about the difficulty level - I do, however, with the deadline system, since a lot of people taking these classes (including me) don't have the flexibility to spend the necessary amount of time every week, and once you are one week behind unless the class is under your level it's hard to catch up.

And people who know the material should be able to answer those questions without any difficulty. What's the problem?

Perhaps I've been out of formal education for too long (esp. on the mathematical side of things), but I did find the material quite challenging, more so the problem sets than the programming challenges. Part of that was emotional, I didn't do a computer science degree and thus find this kind of stuff almost... intimidating. It's the sort of stuff that gets asked at 'serious' interviews and thus I've had a sense of it being somehow too clever, beyond me.

It's not helped by posts like this, the arrogant 'oh it was all too easy' tone is frustrating. Not everything in life is a competition, and as others have said, it ought to be about actually learning something, not proving how wonderfully clever you are.

What's particularly frustrating in all of this is that I found this particular course wonderfully lectured, and the material fascinating. Reading stuff like this takes away that sense of fascination at the material and replaces it with a sense of being a not quite good enough participant in a competition I didn't want to be a part of.

/rant

I agree. The PGM class is much more challenging than the machine learning and algorithm classes, even though (or perhaps, because) Tim and Andrew are doing such a terrific job with the videos of ML and Alg, respectively. One also notices a steep decline in the number of PGM quiz attempts in the published statistics. However, I think that some of the difficulty of the PGM class should be attributed to the rough edges in the programming assignments. We rely heavily on the forums to collaboratively decipher them (as well as some ambiguous questions on the problem sets). But then, this also shows that Coursera's platform enables positive student interaction...

I probably used every curse I know while working on the 5th PGM assignment, but I am the first to admit that I did learn a lot, and I'm sure they will fix the rough edges for the second run of the class. Moreover, the entrepreneur in me is very inspired to see fresh-from-the-oven code. Sure, I've already seen apps that were lacking some features because they were released fast, but here I have an opportunity to actually see some fresh code (since we are basically asked to "fill in the blanks" in Daphne's implementation). I admire Daphne's entrepreneurial courage to publish something even if is is not 100%.

Also note that the PGM class has many more pointers to recent research in the field [1], which I think the OP would find interesting.

Finally, I think the OP is missing a bit of the bigger picture - comparison of undergraduate studies in Israel and the US. I could probably write a long post about this one day (I've taught science undergrads in both college systems), but at least I should point out a couple of things. In Israel, there is much more focus on the major; about 95% of the classes are in the major field of studies. (One chooses her/his major before applying to college). There are (almost) no GE classes [2]. There is probably no one attending CS classes who does not major in CS (or double major). There are advantages and disadvantages of each system. But as a result, you can put more challenging content into CS classes in Israel.

Therefore, it would probably be more fair to compare a CS class from the Technion to upper-division or first-year grad class. That is, instead of comparing it to Stanford's CS161 (or any other 100-199 classes), it would be more fair for the OP to compare it to the level of CS228/CS229A (or almost any other 200-299 classes).

[1] Not all of these are plugs to Daphne's research- I even remember some pointers to Thruns' papers. (he's sort of a competitor.)

[2] Perhaps this is to allow 3 years college, and save some time we spend serving in the army.