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.
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.
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.
Inhaltsangabe
Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks - Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes.
Chapter 1. Functions Tutorial. Chapter 2. Logic and R. Chapter 3. Sets with R: Building the Tools. Chapter 4. Probability. Chapter 5. Naïve Rule. Chapter 6. Complete Bayes. Chapter 7. Naïve Bayes Classifier. Chapter 8. Stored Model for Naive Bayes Classifier. Chapter 9. Review of Mathematics for Neural Networks. Chapter 10. Calculus. Chapter 11. Neural Networks - Feed Forward Process and Back Propagation Process. Chapter 12. Programming a Neural Network using OOP in R. Chapter 13. Adding in a Bias Term. Chapter 14. Modular Version of Neural Networks for Deep Learning. Chapter 15. Deep Learning with Convolutional Neural Networks. Chapter 16. R Packages for Neural Networks, Deep Learning, and Naïve Bayes.
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