Mathematics and R Programming for Machine Learning
From the Ground Up
- Available for pre-order. Item will ship after October 8, 2020
Based on the author’s experience teaching data science for more than 10 years, Mathematics and R Programming for Machine Learning reveals how machine learning algorithms do their magic and explains how logic can be implemented in code. It is designed to give students an understanding of the logic behind machine learning algorithms as well as how to program these algorithms. Written for novice programmers, the book goes step-by-step to develop coding skills needed to implement algorithms in R.
The text begins with simple implementations and fundamental concepts of logic, sets, and probability before moving to coverage of powerful deep learning algorithms. The first eight chapters deal with probability-based machine learning algorithms, and the last eight chapters deal with artificial neural network-based machine learning. The first half of the text does not require mathematical sophistication, although familiarity with probability and statistics is helpful. The second half is written for students who have taken one semester of calculus. The book guides students, who are novice R programmers, through algorithms and their application to improve the ability to code and confidence in programming R and tackling advance R programming challenges.
Highlights of the book include:
- More than 400 exercises
- A strong emphasis on improving programming skills and guiding beginners on implementing full-fledged algorithms.
- Coverage of fundamental computer and mathematical concepts including logic, sets, and probability
- In-depth explanations of the heart of AI and machine learning as well as the mechanisms that underly machine learning algorithms
Table of Contents
1. Functions Tutorial
2. Logic and R
3. Sets with R: Building the tools
5. Naïve Rule
6. Complete Bayes
7. Naive Bayes Classifier
8. Stored Model for Naive Bayes Classifier
9. Review of Mathematics for Neural Networks
11. Neural Networks -Feed Forward Process and Back Propagation Process
12. Programming a Neural Network using OOP in R
13. Adding in a Bias Term
14. Modular Version of Neural Networks for Deep Learning
15. Deep Learning with Convolutional Neural Networks
16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes
William B. Claster is a professor of mathematics and data science at Ritsumeikan Asia Pacific University in Japan, where he designed the data science curriculum and has run the data science lab since 2008. He has been recognized for his research in data science applied to the fields of medicine, social media, and geoinformatics. His research includes political analysis, stock market forecasting, tourism, and consumer behavior with machine learning applied to social media data. Originally from Philadelphia, he moved to Japan where he has been a resident there for over 20 years. In addition to research, his interests include Japanese architecture, Buddhism, and philosophy.