Elements of Statistical Learning Solutions

Elements of Statistical Learning Solutions

I'm currently working through The Elements of Statistical Learning, a textbook widely regarded as one of the best ways to get a solid foundation in statistical decision theory, the mathematical underpinnings of machine learning.

After starting, it became clear to me why the book has built up such a reputation! The text begins from the very basics of function approximation and rigorously works its way up to more advanced models such as random forests and neural networks. It doesn't just spew out formulae, but supplements every topic with examples and practical discussions. Best of all, the book is FREE!

The Problem

Still, just as with any textbook, it's not a quick and easy read. In fact, this text might be harder to work through than most, due to the amount of prerequisite knowledge required. Furthermore, while the authors strive to give their readers intuition about each topic through examples and discussion, there are many equations/formulae the authors don't elaborate on, or worse, leave as exercises to the reader.

The Solution

Fear not! Armed with the magic of RMarkdown (and too much free time), I've decided to go through the book and fill in the gaps by completing proofs and doing the exercises.

Right now, this list is pretty barren (I just stared), but I'll be periodically adding as I move through the book:

For Now, Use This

Of course, I'm not the only person to ever go through this text to provide notes and solutions! Here is a list of other (more complete) notes and solutions. Feel free to use these while I slowly work through the text myself. You won't hurt my feelings, promise!