I was interested so I looked them up:

A First Course in Probability: http://www.amazon.com/First-Course-Probability-9th/dp/032179...

Linear Algebra with Applications:

http://www.amazon.com/Linear-Algebra-Applications-Otto-Brets...

Note that the Bretscher book has really terrible reviews, but I can't evaluate if the reviews are correct or not.

There is a large mathematical foundation behind machine learning, that is not even taught to most computer science students. The concepts that built machine learning are often found in engineering and mathematics:

It's not easy to learn, especially if you are not strong in math, but if you want an intuitive understanding as to how machine learning works, I would recommend learning a combination of probability, linear algebra, and formal theories of computation (abstract machines):

This book was really what opened the gate of tying the content together:

http://www.amazon.com/First-Course-Probability-9th-Edition/d...

You should also be strong in discrete mathematics.