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marcusboosteronNov 21, 2009
* http://webcast.berkeley.edu/course_details_new.php?seriesid=...
cs702onJune 29, 2018
* fast.ai ML course: http://forums.fast.ai/t/another-treat-early-access-to-intro-...
* fast.ai DL course: part 1: http://course.fast.ai/ part 2: http://course.fast.ai/part2.html
The fast.ai courses spend very little time on theory, and you can follow the videos at your own pace.
Books:
* The best books on ML (excluding DL), in my view, are "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani, and "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. The Elements arguably belongs on every ML practitioner's bookshelf -- it's a fantastic reference manual.[b]
* The only book on DL that I'm aware of is "Deep Learning," by Goodfellow, Bengio and Courville. It's a good book, but I suggest holding off on reading it until you've had a chance to experiment with a range of deep learning models. Otherwise, you will get very little useful out of it.[c]
Good luck!
[a] Scroll down on this page for their bios: http://course.fast.ai/about.html
[b] Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/
[c] http://www.deeplearningbook.org/
ananthrkonNov 4, 2009
Practical Foundations of Mathematics http://www.cs.man.ac.uk/~pt/Practical_Foundations/index.html
Linear Algebra - Gilbert Strang (Book & Videos)
Linear Algebra and Applications http://www.math.unl.edu/~tshores1/linalgtext.html
P.S. There was another Introduction to Statistics book that I am not able to find the link for.