Wednesday, July 22, 2009

What are good prequisite textbooks for someone interested in machine learning?

As mentioned in the last post, machine learning requires a good amount of background knowledge just to make sense of the topic. The core subject areas are:
  • calculus
  • linear algebra
  • combinatorics / discrete math
  • probability
  • statistics
  • information theory
  • real analysis
  • optimization theory

The following is a list of basic, introductory textbooks for each of these subjects :
  • A First Course in Probability by Sheldon Ross. Additionally helpful is William Feller's An Introduction to Probability Theory and Its Applications, Vol. 1.
  • For real analysis, Marsden and Hoffman's Elementary Classical Analysis is pretty standard. It's not a great book though. I find it annoying that the proofs are left for the ends of the chapters.
These books are very basic, at the undergraduate level for the most part. However, if, like me, you weren't a math or engineering major, you will probably find them useful for quickly filling in the gaping holes in your education. Later I'll post some introductory books at the graduate level.

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