I disagree (having taken the course as an undergraduate and it being my first major exposure to machine learning). Certainly if all you do is attend the lectures, you're going to miss some background knowledge, but that is true of most (if not all) university courses. You're supposed to devote 2-3 hours of outside work for each hour of lecture. Meaning 6-9 hours of studying per week outside of those lectures.
Some of this is doing the projects, although some of it is personal investigation.
There are failings of his course (one of the biggest at this point is that it doesn't do any work with the state of the art now), but I think that the fact that his course caters toward people who are self-driven is not a failing.
The best way to look at what the goal of the course is is by looking at his exams. If they weren't different than you took them, they were intentionally too difficult for the allotted time, leading to low averages and incomplete work by the majority of students.
However, the course allows motivated students to make connections between concepts, with the help of the professor and the coursework. Having someone "leading you" down the right path is very helpful, much moreso than a textbook alone.
I really do think that there is one exam question that sums up Isbell's course perfectly: its the one where you are asked to compare and contrast 4-5 aspects of 4 randomized optimization algorithms (RHC, GA, SA, and MIMIC) and explain situations where you'd use each and why.
The course's goal is to lead to a strong intuition for the algorithms covered (sadly at the partial expense of a theoretical understanding), not everyone puts in the work to develop that understanding, but that's not a failure of the course, necessarily.