[edtechre]

That's partly why I bought it. My university didn't have a statistics department, so I never had the chance to take a statistics course that was worthwhile.

Statistics and linear algebra really should be required by all CS programs. It's funny that at many schools those courses are not, yet Calculus is. First, Calculus should have been handled in HS. Second, I've never had a use for Calculus professionally or for anything I've worked on in my free time.

[stiff]

Calculus is a major prerequisite for both a reasonably serious statistics course, and for a reasonably serious AI/ML course. Furthermore, at least for ML it is multi-variable calculus based on linear algebra, I doubt you could learn this in high school at the level needed.

[edtechre]

And on that note, does anyone have any other good book suggestions regarding ML?

[maurits]

Of the top of my head,

- Machine Learning by Tom M Mitchell http://www.cs.cmu.edu/~tom/mlbook.html

For general reading and introductions I also like:

- Pattern Classification by Richard Duda

- Pattern Recognition and Machine Learning by Christopher Bishop

For a bit more emphasis on statistics and math, I usually dive in to

- Classification,Parameter Estimation and State Estimation by van der Heijden

And last, but certainly not least:

- Information Theory, Inference, and Learning Algorithms by David MacKay, available here:

http://www.inference.phy.cam.ac.uk/mackay/itila/

[edtechre]

Excellent, many thanks.

I've read O'Reilly's Collective Intelligence. It's a great introductory survey, but it was very light on theory.

I also own Collective Intelligence in Action. It had more explanation of theory than O'Reilly's offering, but most of the chapters devolved into how to use Java data mining framework X.