Behavior Analysis with Machine Learning Using R

Behavior Analysis with Machine Learning Using R is a book that introduces machine learning and deep learning concepts and algorithms applied to behavior analysis problems. It focuses on practical aspects of solving such problems based on data collected from sensors or stored in electronic records. Examples demonstrate how to perform common data analysis tasks using the R programming language and real-life behavioral data. The book is intended for undergraduate/graduate students and researchers from various disciplines who want to learn the basics of machine learning and deep learning and apply them to analyze behavioral data.

Format: Paperback / softback
Length: 400 pages
Publication date: 29 January 2024
Publisher: Taylor & Francis Ltd

Behavior Analysis with Machine Learning Using R is a comprehensive guide that delves into the realm of machine learning and deep learning, applying these concepts and algorithms to a wide range of behavior analysis problems. It emphasizes the practical aspects of solving these problems by utilizing data collected from sensors or stored in electronic records. The included examples showcase various data analysis tasks, including data exploration, visualization, preprocessing, data representation, model training, and evaluation, all conducted using the R programming language and real-life behavioral data.

While the examples primarily focus on behavior analysis applications, the underlying concepts and methods covered in the book have broader applications across different domains. No prior knowledge in machine learning is assumed, although a basic understanding of R and basic knowledge in statistics and high school-level mathematics are beneficial.

The book offers a wealth of features and capabilities, enabling readers to build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and more. It also provides hands-on guidance on programming your own ensemble learning methods and utilizing Multi-View Stacking to fuse signals from heterogeneous data sources. Unsupervised learning algorithms are employed to discover criminal behavioral patterns, while deep learning neural networks with TensorFlow and Keras are utilized to classify muscle activity from electromyography signals and detect smiles in images.

Furthermore, the book includes comprehensive evaluation methodologies to assess the performance of models in both traditional and multi-user settings. Anomaly detection models such as Isolation Forests and autoencoders are also built to identify abnormal fish behaviors.

Designed for undergraduate/graduate students, Behavior Analysis with Machine Learning Using R offers a comprehensive and practical introduction to machine learning and deep learning in the context of behavior analysis. It provides a solid foundation for those seeking to apply these advanced technologies to real-world problems and advance their understanding of the field.

Weight: 800g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032067056