SUPPORTING MATERIAL

Dynamic e-Chapters

As a free service to our readers, we are introducing e-Chapters that cover new topics that are not covered in the book. These chapters are dynamic and will change with new trends in Machine Learning. New chapters will be added as time permits.

To access the e-Chapters, please go to this page. Enjoy!

Slides

Lecture slides based on the book and the e-chapters are available here. They are designed for quarter-length and semester-length courses that use the book as a text.

Notations

The LaTex file that lists the notations can be downloaded here.

Video Lectures

Video lectures covering different parts of the book will be posted. The first set of lectures is based on the Learning From Data course. The lectures are available here.

Code

Students in courses and readers of the book have implemented the examples and exercises in a variety of languages, including MATLAB, R, OCTAVE, PYTHON, JAVA, C++. Some publicly-available implementations are as follows.

LIONoso

Various cases in the book can be implemented with LIONoso, a comprehensive Machine Learning and Intelligent Optimization tool for non-profit research and academic use. Some cases that support the presentation in the book are publicly available online at:

LIONoso for "Learning from data"

Octave

• Graphical demo of Perceptron Learning Algorithm (see Chapter 1)
• Graphical demo of Pocket Algorithm (see Chapter 3)

Data

Handwritten Digits

The digits data are useful data for experimenting with some of the techniques discussed in the book, and they are also used in some of the chapter problems.