Ceja E.G. Behavior Analysis with Machine Learning Using R

CRC Press, Taylor & Francis Group, LLC, Enrique Garcia Ceja, 2022. — 432 p. — (Chapman & Hall/CRC The R Series). — ISBN: 978-1-032-06704-9.Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as data exploration, visualization, preprocessing, data representation, model training, and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics is beneficial.FeaturesBuild supervised machine learning models to predict indoor locations based on Wi-Fi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on.
Program your ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources.
Use unsupervised learning algorithms to discover criminal behavioral patterns.
Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images.
Evaluate the performance of your models in traditional and multi-user settings.
Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors.Introduction to Behavior and Machine Learning.
Predicting Behavior with Classification Models.
Predicting Behavior with Ensemble Learning.
Exploring and Visualizing Behavioral Data.
Preprocessing Behavioral Data.
Discovering Behaviors with Unsupervised Learning.
Encoding Behavioral Data.
Predicting Behavior with Deep Learning.
Multi-user Validation.
Detecting Abnormal Behaviors.Setup Your Environment.
Datasets.