Andrew Ng's "Machine Learning" on Coursera is also very well presented. Maybe a bit light on the hardcore Math side (which he acknowledges several times), but he gives a very good overview of what's available, how and when to use different ML techniques. He never loses track of the big picture, which really is one of the most important aspects of tackling any problem space, because in the end you're not going to re-implement a neural network, you'll just use an existing package.
I did the PGM course (successfully) and Daphne Koller warns us in the introduction that it is a hard course (even by Stanford CS standards) and Stanford students do spend a significant amount of time to it weekly ( I think it was 15-20 hrs avg). I did indeed often get lost in some of the ramblings where I was thinking "why is this necessary and what are we trying to do here?". It was not always clear to me how some of the techniques connected to reality, and why it was better than others. But still a very useful course going deep into Bayesian networks, Markov fields, etc...
So, your mileage may definitely vary, and some courses really do require you to be on top of your game and have some serious prior background knowledge. But I love MOOCS and how can one not be thankful to get access to courses given by the most prominent researchers and profs in their field?

Except for maybe hardcore nerds, I've noticed most people in STEM actually are very interested in Liberal Arts ( Literature, Music, Anthropology, History, Graphical Arts, ...) and enjoy experiencing and learning about it on their own time. Of those people who were into STEM in high-school, most achieved higher grades in the Liberal Arts courses given in high school than the so called liberal arts students.

Indeed. I know a 40 yr old woman who's Director in a technology company (a real director, not a startup 10 man company 'director', meaning big payout, big BMW company car) and she's also complaining about the glass ceiling because all collegues she works with at about the same level are VP. She doesn't seem to get that in the last 7 yrs she got promoted from Software Engineer to Project Management to Director. That's a pretty steep promotion curve, and she still has a long career in front of her to make it even bigger. I'm sure of the former software engineers she worked with most are still just that.

Also, WTF has this to do with the Tech World and gender discrimination in Silicon Valley? This just sounds like a regular vanilla company discrimination issue. Well, maybe not Vanilla, but Big Money Big Business company.

Well, Machine Learning doesn't exclude the use of Semantic Tools like Ontologies. You can still use them to gazeteer your ML indexing process, inference over the Ontology hierarchy etc... Both aren't really mutually exclusive. However, I do think the idea of everyone annotating their webpages semantically is never going to take off. The closest thing we have successfully achieved on the interwebz in that sense is WikiPedia.

No you didn't :) It was a valid argument.
However this semantic enhancement requires a couple of things: the model (ontology) must be defined by consensus. A model is by definition an incorrect representation of reality. Hence even with a manually crafted model ontology, it still won't be 'exact'. If you apply this on big medical ontologies, you're really in trouble, as they may have hundreds of thousands of concepts. So this is the ontology part. Next you have the actual semantic annotation part of the document where you put actual trust in the annotator that his knowledge of the ontology is perfect and he's doing a good job of annotating the document. This requires plenty of training.

To clarify, I don't think statistics (ML) would give a better model than an 'exact' - manual - model. I was more speaking in the sense of a 'good enough' system which is also scaleable.

I think there are two reasons why the whole rdf(s)/owl annotated web pages never really gained traction. First of all it's hard work if you have to do it manually, but most content management systems now offer some kind of key word adding feature though. The second reason, IMO, is that the current Big Data and Machine Learning techniques (and more computing power / persistence media / bandwidth than 15 years ago when the whole rdf/owl thing took off) trump the whole categorization and knowledge extraction / data mining process anyway.

As Socrates once said around 500 bc : "Our youth now love luxury. They have bad manners, contempt for authority; they show disrespect for their elders and love chatter in place of exercise; they no longer rise when elders enter the room; they contradict their parents, chatter before company; gobble up their food and tyrannize their teachers."

How about people that took the test with a linux machine? That's a stat available from the browser info as well, no? Didn't check on that now, did they?

They are all infringing (it's NOT stealing!).

The same happened to Marie Curie (you know, the only person to date to win two nobel prizes in different categories and the first to win two alltogether). She was not able to enroll in higher education in Poland because it was not accepting women. Luckily she went to Paris ...

For the 'harder' coding silence is definitely golden. But for the monotone 'this is the boring part' coding, just give me a couple of Tool CDs, they are great for keeping focus.

This time it's different though.
Because of the Arab Spring, Western Muslims started to be more engaged around freedom in the Arab countries.
The civil war in Syria (where at first freedom fighters started fighting against the Assad government) succeeded in actually engaging young and influenceable Western Muslims on the field who struggled with their mixed Western and Muslim identity.
ISIS was very successful in utilizing that momentum to draw more young Western Muslims into their ranks later on and many tens of thousands European young muslims (many of them under-age) are now fighting with ISIS.
This is very worrisome because these can get back into Europe since they have passports and under the Visa-Waiver program could just board a plane to the US for example.
In that sense, this is now a global conflict. It's not just some tribes fighting against each other and the US intervening to keep control of the oil. It's an exodus of young people who are getting brainwashed and are ticking time-bombs when/if they get back to their actual Western home country.

I don't know exactly which CS curriculum these people went through that didn't have to "work hard enough to understand something", but I'm pretty sure if you have to take (distinct) math classes like Differential and Integral Calculus, Abstract Algebra and Group Theory, Linear Algebra and Geometry; and next to that some other fundamental CS theoretical classes including Lambda Calculus, etc... all in the first year of your CS curriculum (which at least was the case here); you probably encountered a couple of hard nuts to crack.
Add to the mix in the later years a couple of more "applied" CS courses like compilers, interpreters and parsers; and of course; what everyone here is referring to as "good programming" some "easier" classes about Software Engineering, OO programming, Design Patterns and Databases, Networks, and that's basically what a CS curriculum looks like here (except from some possible AI, Machine Learning, Management, ... courses).
Of course, this still doesn't guarantee you a good Software Developer/Engineer (passion and motivation is very important), but the basis is as good as it gets.

So, Again, unless you guys have some university level CS curriculum where you graduate building a website in PHP, or unless these Javascript weenies actually had no CS background at all, I'm a bit weary of your statement.
Mind you, I hold EEs in high regard and would definitely hire even if not up to par with latest Node.js FizzBuzz framework.