Learning Machine Learning – Intro & Roadmap

(Last revision: 2014-09-30. Added Foundation Courses section.)

I’ve been having lots of fun with Machine Learning lately. It all started with taking Andrew Ng’s excellent Coursera Machine Learning course. Andrew is a fantastic instructor, with a knack for conveying complex concepts while not getting you bogged in not-yet-relevant details that’d take away from your getting the big picture.

While taking the course, I’ve been creating a lot of draft posts, mostly about digging deeper into topics on which I’m still shaky. Most of the times, this is because of the math involved. Machine Learning requires a lot of math, and it’s on a level that’s way, way deeper than the math you’ll ever see in the CFA curriculum.

(That said, if you’re a CFA candidate or charterholder, don’t let this discourage you! The math you encounter in the CFA curriculum serves as excellent foundation to the math you’ll see in Machine Learning. Here’s a quote from the Preface of Applied Predictive Modeling book: 

For this text, the reader should have some knowledge of basic statistics, including variance, correlation, simple linear regression, and basic hypothesis testing (e.g.: p-values and test statistics).

That’s level 2 all over again, folks! 🙂 )

As part of my learning process, I’d like to create a collection of posts about Machine Learning, with the hope that the process will strengthen my own understanding, and maybe (who knows!) even help someone who’s interested in the topic, but lacking the mathematical tools and a good roadmap to follow to get better at this (i.e.: just like where I am today).

Roadmap

  1. Take Andrew Ng’s Machine Learning Course. (DONE)
  2. Go through Applied Predictive Modeling book, and pick up R language in the process. (ONGOING)
  3. (To be determined)

Math Basics

Resources

Foundation Courses

  • Probabilistic Systems Analysis and Applied Probability. I wish there were something like this when I was still in school/university back then (yes, that was a long time ago). This is an excellent course that you can take at your own pace, and it’s as good as it gets, complete with quizzes, practice problems, and solutions.

Machine Learning Courses

Machine Learning Books

  • Applied Predictive Modeling. I’m currently reading this book. I bought it because it’s (1) an introductory and (2) a very hands-on book (at least the reviews say so).
  • An Introduction to Statistical Learning: 103. A book with equally glowing reviews in Amazon. I picked Applied Predictive Modeling first because (again, from the reviews) it provides a more complete coverage of the entire process.
  • The Elements of Statistical Learning. Available in its entirety in PDF. The math is a bit too advanced for me though. There are just a bit too many foreign words for me to be distracting. I can persevere and look the words up one by one, but I suspect I’d have a more productive time with a gentler book (hence my choice to go with the Applied Predictive Modeling book).
  • Pattern Recognition and Machine Learning. Another book of which many people speak very highly. I did not pick this book because it’s not available in soft copy (unlike the AML book, which has a Kindle version that’s supposedly an exact replica of the printed book), and most importantly, I get the sense that it’s more advanced than the AML book.
  • (To be updated)
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