I did the Stanford free online one the first time it was offered a year or so back. Was perfect -- didn't move at a blazing pace and was very lean. Great instructor, highly recommended (though I think it may have been absorbed into Coursera?).
Do you have a link? I would be interested.
CMU's Introduction to Machine Learning Course
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Re: CMU's Introduction to Machine Learning Course
#12It's worth noting that very many CMU courses, from all departments, have notes available on a publiclly-accessable course website. Source: Am a CMU 2012 grad.
I think you meant noting* ... haha
Re: CMU's Introduction to Machine Learning Course
#13How is this course compared to Andrew Ng's Coursera class, his regular Stanford class and Caltech's Learning from Data course? (Other ML courses available on the web in terms of depth)
Re: CMU's Introduction to Machine Learning Course
#14You may want to look at the self-evaluation, a qualification exam for understanding the material:
http://alex.smola.org/teaching/cmu2013-10-701x/slides/Intro_...
It would have been a piece of cake in college, but I have not used that math in a long time :-/
Re: CMU's Introduction to Machine Learning Course
#15How is this course compared to Andrew Ng's Coursera class, his regular Stanford class and Caltech's Learning from Data course? (Other ML courses available on the web in terms of depth)
I can't say how this specific CMU course compares with Ng's online course, but I can comment on Ng's course as compared to several other similar courses I took in Nanyang Tech. U. and Paul Sabatier (Toulouse 3), and my overall remark is that Ng's course is quite short on the maths, which makes it not sufficiently formal to deeply understand what goes on. However, it gives enough material and code samples to play with data. It can be nicely complemented with some self-study.
Re: CMU's Introduction to Machine Learning Course
#16I did the Stanford free online one the first time it was offered a year or so back. Was perfect -- didn't move at a blazing pace and was very lean. Great instructor, highly recommended (though I think it may have been absorbed into Coursera?).
Yes, I'm taking it now. Edit: https://www.coursera.org/course/ml
There's also a more sophisticated course on ML by Hinton: https://www.coursera.org/course/neuralnets
Have you tried it as well?
Re: CMU's Introduction to Machine Learning Course
#17I think now it is the time we get some tutorial/resources/classes on practical implementation of these ML techniques. Enough of Introduction to ML. How to handle large data (say 6000000 rows), how to convert csv/tbv data to different formats needed for different machine learning libraries for e.g. Weka, LibSVM etc.
For an introduction to the broader realm of data input, normalization, modeling, and visualization -- in which ML plays but a part -- you can "preview" Bill Howe's "Introduction to Data Science" class on Coursera[0]; I'm working through the lectures, and I find he gives compelling explanations of what all these parts are, why they're important, and how it all fits together in a larger context.
Re: CMU's Introduction to Machine Learning Course
#18That is not the intro course. This is http://curtis.ml.cmu.edu/w/courses/index.php/Machine_Learnin...
Re: CMU's Introduction to Machine Learning Course
#19For comparison, here is MIT 9.520:
Statistical Learning Theory and Applications http://www.mit.edu/~9.520/
I think it is interesting how different ML courses can have such different emphasis in content. The MIT course is all about regularization.
Re: CMU's Introduction to Machine Learning Course
#20How is this course compared to Andrew Ng's Coursera class, his regular Stanford class and Caltech's Learning from Data course? (Other ML courses available on the web in terms of depth)
I can't say how this specific CMU course compares with Ng's online course, but I can comment on Ng's course as compared to several other similar courses I took in Nanyang Tech. U. and Paul Sabatier (Toulouse 3), and my overall remark is that Ng's course is quite short on the maths, which makes it not sufficiently formal to deeply understand what goes on. However, it gives enough material and code samples to play with…
It's worth clarifying that Ng's online course is essentially his Stanford course minus most of the math. The online version doesn't have the proofs or theory problem sets, while the Stanford version does. The problem sets, etc... are available at cs229.stanford.edu, if anyone is interested.