First Course in Probability, A
Description: A first course in probability by Sheldon M. Ross provides an introduction to probability theory with clear explanations, diverse examples, and problem sets designed to build foundational understanding in the subject
ISBN: 032179477X
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drcomputer
· 2014-12-19
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Original thread
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.
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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.