Short description:

The book includes detailed Python examples for each phase, making it an essential resource for researchers and practitioners in animal behavior and technology.

Long description:

This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python.

The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis.

Table of Contents:

Preface. 1. Introduction to Machine Learning for Farm Animal Behavior  2. Machine Learning Concepts and Challenges.  3. A Practical Example to Building a Simple Machine Learning Model  4. Sensors, Data Collection, and Annotation  5. Preprocessing and Feature Extraction for Animal Behavior Research  6. Feature Selection Techniques  7. Animal Research: Supervised and Unsupervised Learning Algorithms  8. Evaluation, Model Selection and Hyperparameter Tuning  9. Deep Learning Algorithms for Animal Activity Recognition  References