I don't know if the online classes will cover the same, less, or more material than the equivalent classes at Stanford. However, if they cover the same material, both classes combined are about half of a full-time student's workload in terms of academic units, but SUBSTANTIALLY more than half of a full-time's student's workload in terms of effort/hours. The material is difficult and the assignments are challenging. Expect to spend 10-20 hours of intensive work per week on each class.

I took the ML class in Autumn quarter and the AI class in Winter quarter of last year, there is definitely a lot of overlap - although to that extent, it might make it easier since you won't have two disjoint topic sets to study.

ML definitely goes into more mathematical rigor, while AI tends to cover a wide range of topics in enough depth to know when to apply them and how to interpret results.

I wonder the same and am having a tough time deciding which to take (or both).

for what it's worth, if you take the ML class, you will learn most of the things you would learn in the AI class and more - although it does get a bit rigorous, and will take more time than the AI class would. from personal experience, I feel like Ng's class gave me a more thorough foundation in the math behind the concepts, and was more challenging to boot - so I'd recommend it if you're feeling up for it.

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?

Any opinions greatly appreciated.

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.

For now I signed up for both: if it will consume too much time I can drop one of them later, keeping more interesting one.

here's a post on the topic from reddit

http://www.reddit.com/r/mlclass/comments/krg3p/whats_the_dif...

Same here. I'm thinking ML since it will have programming exercises. I'd likely forget all of the theory otherwise.

For what it's worth, most of the programming in the ML class when I took it last year was MATLAB based - since it mostly involved lots of linear algebra, and using MATLAB meant you got matrix operations for free. AI class, on the other hand, involved coding an increasingly more intelligent Pacman agent in Python as we learned new approaches to optimizing reward.

Having said that, taking ML definitely made my theory stronger - but this was mostly due to the amount of proofs in the class, and not so much the programming :)

Interesting point.

http://www.ai-class.com/overview says: "Programming is not required, however we believe it will be very helpful for some of the homework assignments. You may write code in any language you would like to (we recommend Python if you are new to programming) and your code will not be graded. For example, a question might ask for 6 answers to the same problem but with varied inputs or parameters. You are welcome to work each one out by hand, however writing a program might be both faster and give you a better understanding of how the algorithm works."

So there will be at least some programming, they got me confused - how is it possible to teach AI without programming? :) But we'll see.

The idea is that they won't set problems where the answer to be supplied is a program, but they will set problems which would be tedious or impractical to answer without writing code to help you arrive at the answer.