Machine Learning Course Materials

http://cs229.stanford.edu/materials.html

by cdl • 13 years ago

125 13 13 years ago

cs229.stanford.edu

fintler• 13 years ago

Stanford's CS229 was also on Coursera.

https://class.coursera.org/ml/lecture/preview

firaga01• 13 years ago

Actually, the Courersa class is closer to another course taught by Ng: CS229A - Applied Machine Learning. CS229 is far more theoretical.

dvdhsu• 13 years ago

I've posted about this many times, but it's worth noting that the Machine Learning course on Coursera is CS229A(plied), which is different from CS229 the one most Stanford students take. The Coursera one is more useful if you want to apply machine learning; the Stanford one linked by the poster is more useful if you want to enter the field. The Coursera one glosses over a lot of the mathematics behind the algorithms; the Stanford one delves into the mathematics supporting the algorithms.

Here are the complete corse materials (including video lectures!) for CS229, courtesy of Stanford Engineering Everywhere: http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a...

signa112• 13 years ago

i did take the cs-229A when it was first offered on coursera, and your comment is exactly right. most of the mathematics was glossed over, and the focus was on not "worrying" about it, but on implementation (of various algorithms) using octave, and observe the results. which for neural-nets mostly boiled down to some slightly complicated matrix-multiplication.

having said that, it was an excellent overview of a broad spectrum of ml techniques, and i for one, would heartily recommend it to anyone with high-school background in maths, and a deep interest in the field. supplementing the material with simon-haykin's text or christopher-bishop's (most excellent) book, would make it slightly tougher.

unfortunately, due to some time constraints i could not partake on geoff-hinton's wisdom on deep-learning. would you happen to have material for that stashed somewhere ?

muyuu• 13 years ago

I did that course and it's very different. They are complementary, the one in Coursera being more introductory.

I'd recommend the Caltech course too, which is also tougher.

pauloortins• 13 years ago

I wrote a blog post with some material suggestions to learn Machine Learning. If you want, visit my blog post:

http://pauloortins.com/resources-to-become-a-ninja-machine-l...

tomcrisp• 13 years ago

For a second, I misread this as "Machine Learns Course Materials". Must make this happen - wonder if I can use these machine learning course materials to help.

gtani• 13 years ago

As a reference, you'll want one or two of the Big 6 texts, by Murphy, Koller/Friedman, Bishop, MacKay, and Hastie et al ESL. The first review is good http://www.amazon.com/product-reviews/0262018020/ref=dp_top_...

Also, there are many freely available texts on ML, data mining, stats/prob distributions, linear algebra, optimization etc, incl Barber, Mackay and ESL. See http://www.reddit.com/r/MachineLearning/comments/1jeawf/mach...

Machine Learning Course Materials

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