- 2 reviews
- 2 completed
TL;DR: bad structured, not in-deep course, ask you to solve assignments using tools not explained. I studied Artificial Intelligence and have background in ML/DM methods, as well as intermediate programming skills using Python. My general impression is that the contents of this course are not well structured, they don't really connect (even if the instructor insists in so) and all in all it looks like a bunch of slightly related material, poorly covered. The in-lecture questions are not well-crafted, often they just ask you about random stuff you might have learned somewhere else. The students are asked to do homework using tools not explained in the lectures, at all. For example, perform Bayesian Network inference using SQL, when SQL is not explained in the course... And the last assignment is a data-mining task of a huge file, using tools not explained at all (Orange, Octave...?) you just have to learn to use on your own.
My background: former Artificial Intelligence master student. I took this course to refresh some concepts on Machine Learning (covered extensively in my master program) and ended up learning more. The instructor does very good at teaching the concepts in an easy way for any background level, with lots of detail. There is also a lot of work put in preparing the assignments. The only "but" I see to this course is that you are allowed to attempt the quizzes so many times and the programming assignments are sooo guided that (a) all students will have a very high grade, not differentiating at all and (b) you might end up learning a lot less because everything is almost done in the assignments.