2. Predicting Behavior with Classification Models
3. Predicting Behavior with Ensemble Learning
4. Exploring and Visualizing Behavioral Data
5. Preprocessing Behavioral Data
6. Discovering Behaviors with Unsupervised Learning
7. Encoding Behavioral Data
8. Predicting Behavior with Deep Learning
9. Multi-User Validation
10. Detecting Abnormal Behaviors
Appendix A. Setup Your Environment
Appendix B. Datasets
Biography
Enrique is a Data Scientist at Optimeering. He was previously a Researcher at SINTEF, Norway. He also worked as a PostDoc at the University of Oslo. For the last 11 years, he has been conducting research on behavior analysis using machine learning. Feel free to contact him for any questions, comments, and feedback.
e-mail: e.g.mx [at] ieee.org
twitter: https://twitter.com/e_g_mx
website: http://www.enriquegc.com
"The book presents a significant multifold contribution to the modern state of art of artificial intelligence: it combines methods of machine learning and deep learning neural networks, automatic data reading and processing from sensors of various kind, and the R tools permitting to perform all the needed estimations. Clear exposition of numerous methods makes the book valuable for undergraduate and graduate students who want to learn the fundamentals of machine learning and deep learning, and for more experienced researchers who want to apply machine learning to analyze data from different disciplines. And last but not least—the book is illustrated with multiple color comics strips which make reading even more pleasant."
– Technometrics"Behavior Analysis with Machine Learning Using R seamlessly integrates (1) an introduction to machine learning, (2) how to build machine learning models with R, and (3) how to apply these models to human behavior. The text is well written, concise, clear, and engaging – one of the best introductions to the topic of machine learning that I have read. The methodology is clearly explained without getting bogged down in the mathematics. At the same time, the code examples clearly demonstrate the underlying mechanics of each approach. The examples are fresh and unique. Anyone interested in machine learning will find the book valuable. For those interested in human behavioral analysis with R, there is no better book currently available."
"In summary, this is an excellent book for those who attempt to use machine learning methods to deal with human behavioral data with R. The book covers the most popular methods and essential topics in machine learning while providing analysis examples implemented in the R program."
- Robert Kabacoff – Professor, Wesleyan University, United States of America; author of R in Action
- Charlotte Wang, National Taiwan University, Biometrics, September 2022
