

Ask HN: Good resources for learning about ML/AI? - aviraldg

Hey HN.<p>I've recently started learning about Machine Learning from Stanford's CS229 lecture videos and I was wondering if people with more experience could point me to (preferably free) supplementary resources for the same?
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glamp
Berkley Course: <http://alex.smola.org/teaching/berkeley2012/statistics.html>

Scikit-Learn Docs: <http://scikit-learn.org/dev/>

Google Research: <http://research.google.com/pubs/papers.html>

Caltech Course: <http://work.caltech.edu/telecourse.html>

Basics in R: <http://www.statmethods.net/advstats/glm.html>

R packages for data science: [http://blog.yhathq.com/posts/10-R-packages-I-
wish-I-knew-abo...](http://blog.yhathq.com/posts/10-R-packages-I-wish-I-knew-
about-earlier.html)

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r41nbowcrash
Weka: <http://www.cs.waikato.ac.nz/ml/weka/>

Lets you test dozens of different algorithms on a given dataset, to see what
works and what not, in like one evening.

Also success stories from Kaggle competition:
<http://blog.kaggle.com/category/dojo/>

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m_ke
<http://videolectures.net/Top/Computer_Science/> has a ton of lectures and
conference talks from the top researchers in ML and AI

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jclos
Jenn Vaughan's class notes at <http://www.cs.ucla.edu/~jenn/courses/F11.html>
are really good in my opinion.

