Unfortunately the "difficulty" of the ML class assignments was not in the material application of the things learned in the lecture but instead in the gotchas of Matlab/Octave vectorization. Most of the assignments were in the format of: "Here's a forumla that applies to the element, generalize it to the matrix so there's no for-loops." Which, while challenging for those not accustomed to thinking that way, was not a challenge of applying what one learned from the lectures.
The course was awesome for what it was: a chance for the average programmer to get their feet wet in ML, but the fact that it was hosted by Stanford seems to lead to a somewhat humorous irony that it was pretty accessible whereas Stanford's real-life rigor is supposed to be nothing of the sort.
Comments
Unfortunately the "difficulty" of the ML class assignments was not in the material application of the things learned in the lecture but instead in the gotchas of Matlab/Octave vectorization. Most of the assignments were in the format of: "Here's a forumla that applies to the element, generalize it to the matrix so there's no for-loops." Which, while challenging for those not accustomed to thinking that way, was not a challenge of applying what one learned from the lectures.
The course was awesome for what it was: a chance for the average programmer to get their feet wet in ML, but the fact that it was hosted by Stanford seems to lead to a somewhat humorous irony that it was pretty accessible whereas Stanford's real-life rigor is supposed to be nothing of the sort.