> The biggest problem with both statistics and machine learning education, I would say, is their lack of emphasis on mathematical foundations.
Any recommendations for someone who'd like to pick up sound (if modest) foundations?
Any recommendations for someone who'd like to pick up sound (if modest) foundations?
- Linear Algebra Done Right by Sheldon Axler
- Linear Algebra by Steven Levandosky
- Linear Algebra and Multivariable Calculus, and Manifolds by Ted Shifrin (you probably do not need the manifolds part)
Also, as paradoxical as it may sound, CS229 lecture notes themselves. The key here is to verify the details painstakingly. In math, we joke that whenever a book says something is "trivial" or "easy to verify" or "clear to show", it is a substantial exercise in disguise.
The "big problem" is that math is taught in a restricted time scale - a semester, just like all other standard courses. In reality it might take someone many weeks or months to pick up a certain piece of measure theory or hilbert spaces. Math builds upon itself and when it is rushed there is a lack of connection and then ultimately failure to understand.
This is why MOOCs are a great boon for many, as they allow more freedom to self-dictate the pace.
I suspect many mathematicians like being intimidating.
For linear algebra and calculus, at least, Coursera also offers courses.