After it was all over, but before the fat lady sang the grades, this question was posted on the forum site:
Did the top students find the questions ambiguous?Well. I managed to eek out a 96.5% (thanks to a reversal of answer fortune on one of the so-called ambiguous final questions where "they" gave in and accepted both answers as correct). This only put me in the top 25% of the class(!!?). Tough crowd. Here is my reply to the above. (It attracted not a single discussion comment nor have I seen it displayed in the blurbs for subsequent classes):
This forum keeps talking about the ambiguous questions. Perhaps we should be asking “ambiguous to whom?” My hunch is that the top students didn’t find the questions ambiguous, although I could be wrong. To test my hypothesis, perhaps Irvin and the Profs could give some data to work with, and we could (or they could) test this statistically.
Define a “top student” as a student who achieved say 90% or more on the mid-term (or mid-term + homeworks perhaps). Then look at how many got the “ambiguous” questions correct before the alternate solutions were accepted, vs. the non-top-students. If my hunch is right, it will be the non-top-students who found the questions ambiguous, which tells its own story.
It will also mean that the top-students’ rank overall won’t change too much from the introduction of the alternate solutions, as it will lift all those who got say 80% or less to say 85%. The ranking of students up above 90% won’t change a great deal.
Just a hunch.
I too (believe that I) am in the 90%. I found many ambiguities in the lectures, homework, and exams. And I found each one very annoying because I felt I was being graded on how well I could guess what the professors really meant (why I cared about a grade is still a bit beyond me...). My general feeling is that grad students should have taught this course because they are closer to the assumptions being made, senior researchers are bound to "forget" that, e.g., their definitions of Stochastic or Partially Observable are built up over many years of experience. From the beginning certain things were not clearly defined, vis "Rationality" which was only mentioned in the summary of the first video lesson.
My biggest problem with this course was the inability to get answers to questions from definitive sources. First there was no real forum to ask them, and then when aiqus came on line, almost every question went unanswered because it related to open homework or exam questions. I finally realized that there is no point in taking a class where I cannot ask questions and am unlikely to take another online course because of that.
As an example, I got on the wrong track with Particle Filters early on and it was only by dint of repeated reading and listening that I stumbled on the right one. And the only way I knew that I had finally gotten close was that I got the exam questions right. A TA in a study group would have been able to correct my tangent in about 5 seconds...
The chief thing I learned in the class was that I am not very interested in Artificial Intelligence. I discovered that there are sister fields, Computational Intelligence for one, that are more in line with my interests but each has an academic rivalry that keeps them distinct. For instance, there were a couple references to RA Brooks early robotics work (in the book -- I forget if it came up in the videos), especially the 1991 paper "Intelligence Without Representation", which were somewhat dismissive -- because it seems that our professors are still more aligned with the symbolic AI school of thought.
Perhaps if this class had been a survey of the broader field rather than a (kind of) detailed study of specific techniques I would have found it less frustrating. One of my friends who was auditing the class said: There's a bunch of "Oh, that's how they do that" moments but it's missing the big picture.
