the best aspect of the andrew ng course was the homeworks using octave. Yes, it's watered down and not as mathematically rigorous as the real course, but you learn a lot of the essentials from the experience of coding machine learning algorithms, that I can't imagine learning as easily from doing non-programming homework.
Prof. Ng's class wasn't watered down. CS229A(http://cs229a.stanford.edu/) is the Stanford equivalent of the online ml-class. Ng also teaches another machine learning course at stanford(CS229) which focuses on the theoretical underpinnings of ML.
Having taken the online course, I'm not sure I understand you - the programming assignments in some cases required 1 line of code be written (in the middle of a more complex program). I find it really hard to believe that students who pay tuition would be given the same assignments.
CS229A is a course taken by people from different backgrounds not just CS. It basically deals with the practical aspects of machine learning, implementation issues etc. In addition to the lectures and the assignments, stanford students also had an additional course project.
My favorite classes (and the ones I've learned the most from) have always managed to balance theory and implementation. The formula has usually been a mix of
1) purely theoretical exercises emphasizing fundamental concepts
2) project-based assignments in which you must understand the theory and write a decent amount of code to apply it. Usually a bunch of code not central to the concepts has already been written for you. But just implementing it is still not enough. To test your conceptual understanding, they ask you to run your code in various situations and explain the results.
I dunno. You're given the equations in that class. Translating from math notation to octave syntax doesn't require you to have any idea of why any of it works.
I think it's actually quite telling if other institutions feel obliged to ridicule efforts (by Coursera et al) to make online learning a new experience, rather than just copying existing concepts as exercised in traditional universities.
Comments
the best aspect of the andrew ng course was the homeworks using octave. Yes, it's watered down and not as mathematically rigorous as the real course, but you learn a lot of the essentials from the experience of coding machine learning algorithms, that I can't imagine learning as easily from doing non-programming homework.
Prof. Ng's class wasn't watered down. CS229A(http://cs229a.stanford.edu/) is the Stanford equivalent of the online ml-class. Ng also teaches another machine learning course at stanford(CS229) which focuses on the theoretical underpinnings of ML.
Having taken the online course, I'm not sure I understand you - the programming assignments in some cases required 1 line of code be written (in the middle of a more complex program). I find it really hard to believe that students who pay tuition would be given the same assignments.
CS229A is a course taken by people from different backgrounds not just CS. It basically deals with the practical aspects of machine learning, implementation issues etc. In addition to the lectures and the assignments, stanford students also had an additional course project.
My favorite classes (and the ones I've learned the most from) have always managed to balance theory and implementation. The formula has usually been a mix of
1) purely theoretical exercises emphasizing fundamental concepts
2) project-based assignments in which you must understand the theory and write a decent amount of code to apply it. Usually a bunch of code not central to the concepts has already been written for you. But just implementing it is still not enough. To test your conceptual understanding, they ask you to run your code in various situations and explain the results.
I dunno. You're given the equations in that class. Translating from math notation to octave syntax doesn't require you to have any idea of why any of it works.
Agreed.
I think it's actually quite telling if other institutions feel obliged to ridicule efforts (by Coursera et al) to make online learning a new experience, rather than just copying existing concepts as exercised in traditional universities.