- 400 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Behavior Analysis with Machine Learning Using R
About This Book
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 are beneficial.
Features:
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- 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 so on.
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- Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources.
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- Use unsupervised learning algorithms to discover criminal behavioral patterns.
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- 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.
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- Evaluate the performance of your models in traditional and multi-user settings.
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- Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors.
This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
Frequently asked questions
Information
1Introduction to Behavior and Machine Learning
1.1 What Is Behavior?
- React. A biological or an artificial agent (or a combination of both) can take actions based on what is happening in the surrounding environment. For example, if suspicious behavior is detected in an airport, preventive actions can be triggered by security systems and the corresponding authorities. Without the possibility to automate such a detection system, it would be infeasible to implement it in practice. Just imagine trying to analyze airport traffic by hand.
- Understand. Analyzing the behavior of an organism can help us to understand other associated behaviors and processes and to answer research questions. For example, Williams et al. [2020] found that Andean condors the heaviest soaring bird (see Figure 1.1), only flap their wings for about of their total flight time. In one of the cases, a condor flew km without flapping. Those findings were the result of analyzing the birds' behavior from data recorded by bio-logging devices. In this book, several examples that make use of inertial devices will be studied.
- Document and Archive. Finally, we may want to document certain behaviors for future use. It could be for evidence purposes or maybe it is not clear how the information can be used now but may come in handy later. Based on the archived information, one could gain new knowledge in the future and use it to react (take decisions/actions), as shown in Figure 1.2. For example, we could document our nutritional habits (what do we eat, how often, etc.). If there is a health issue, a specialist could use this historical information to make a more precise diagnosis and propose actions.
Table of contents
- Cover Page
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- List of Figures
- Welcome
- Preface
- 1 Introduction to Behavior and Machine Learning
- 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
- A Setup Your Environment
- B Datasets
- Bibliography
- Index