| > The results were interesting, but the setups were mostly given to us, and we just had to code an algorithm that was in our notes. Right; I agree. I'm not sure how they would go about making it more challenging though. They can't expect us to go out and collect data ourselves, after all. I suppose they could give us the data, then expect us to code the setup and algorithms up ourselves, but that, too, would become repetitive after a few assignments. > Not to knock the course. I've learned a lot and the professor (Andrew Ng) does a good job. Agreed once again. I knew nothing about machine learning before starting; now I know about neural networks, SVMs, and PCM. It's really cool how much I've learned already, for free, too! I've also signed up for this course, but the quizzes really aren't up to par. As an example: the first quiz question was about training a neural network with too much data, and about whether or not said network would be able to generalize to new test cases. Overfitting neural networks wasn't even mentioned in the lectures; I had to rely on material from Andrew's class to answer the question correctly. This chasm between the lectures and the quizzes is likely because Geoffrey is the one creating the video lectures, but he's not the one creating the quiz questions; he is having TAs do it [1]. Nevertheless, it looks like they're responding to feedback, so hopefully it'll get better with time. 1. https://class.coursera.org/neuralnets-2012-001/wiki/view?pag... |