I was just informed that Stanford's online ML course is based on CS229A rather than CS 229.
From the CS229A home page
"This class' emphasis is on Applied Machine Learning. Concretely, we want to give you the practical skills needed to get learning algorithms to work. Compared to CS229 (Machine Learning), we cover fewer learning algorithms, and also spend less time on the math and theory of machine learning, but spend much more time on the pratical, hands-on skills (and "dirty tricks") for getting this stuff to work well on an application. More of the homeworks will also focus on giving you practice implementing, modifying and debugging learning algorithms, and less on the mathematical underpinnings of machine learning"
the core idea seems to be that this is a simplified and less rigorous version of CS 229. I am not sure of the tradeoffs between acquiring the "dirty tricks" vs getting a solid grasp of the underlying math (which would presumably result from taking CS229)
Can anyone at Stanford tell me about how much difference is there in the perceived relative toughness of CS229 vs CS 229A? Do CS students take 229A or is it more people from the industry and other (non CS/Math) branches?
I can relay some of the things Prof. Ng said in the first meeting of the CS229A class:
229A is definitely meant to be much less mathematically rigorous - he said that he expects some people who don't feel like they're necessarily up to 229 to take 229A first to gain more familiarity with the material. From the people that actually showed up to the lecture, it was mostly CS people - although it's hard to say what the online enrollment is like. It is definitely meant to be useful to anyone who wants to be able to get machine learning algorithms to work, not necessarily understand their finer points.
Thanks a lot. I am fluent in the pre req math and proving theorems and build ML based real world systems for a living. I just wanted to take a classic class and see what it felt like. I think I'll wait for the online version of 229 then (if there is one in the future).
Thanks again. You saved me a lot of time. Much obliged. HN rocks.
From the CS229A home page
"This class' emphasis is on Applied Machine Learning. Concretely, we want to give you the practical skills needed to get learning algorithms to work. Compared to CS229 (Machine Learning), we cover fewer learning algorithms, and also spend less time on the math and theory of machine learning, but spend much more time on the pratical, hands-on skills (and "dirty tricks") for getting this stuff to work well on an application. More of the homeworks will also focus on giving you practice implementing, modifying and debugging learning algorithms, and less on the mathematical underpinnings of machine learning"
the core idea seems to be that this is a simplified and less rigorous version of CS 229. I am not sure of the tradeoffs between acquiring the "dirty tricks" vs getting a solid grasp of the underlying math (which would presumably result from taking CS229)
Can anyone at Stanford tell me about how much difference is there in the perceived relative toughness of CS229 vs CS 229A? Do CS students take 229A or is it more people from the industry and other (non CS/Math) branches?
Any opinions greatly appreciated.