Machine Learning - Introduction

"It is this author’s personal belief that the most important part of machine learning is the mathematical foundation, followed closely by efficiency in implementation details."<p>An introductory ML material in my opinion can be less mathematically rigorous. Emphasis can be on intuitive understanding of principles of various techniques, the strengths and weaknesses of each and the application of ML techniques to various simplified problems for practice. It is easy to get lost in too much Math and loose sight of real world problem solving.

Cool, I'm excited to read this series.<p>Was a little disappointed to see neural networks noted as "classical" with SVMs designated "modern". And nothing about deep learning? Autoencoders? How about different optimization methods--Truncated newton vs gradient descent?<p>Some of the most interesting recent developments in ML seem to be left out, even if it is just an introduction.