Behavior Analysis with Machine Learning and R: A Sensors and Data Driven Approach

Length: 374 pages
Edition: 1
Language: English
Publisher: Leanpub
Publication Date: 2021-07-04

Learn how to leverage the power of machine learning and deep learning to analyze behavioral patterns from sensors data and electronic records. This book shows you how to explore, preprocess, encode, and visualize your data. Learn introductory machine learning concepts and how to train supervised and unsupervised models using R.

This book aims to provide an introduction to machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems.

The book covers topics and practical aspects within the entire data analysis pipeline—from data collection, visualization, preprocessing, and encoding to model training and evaluation. No prior knowledge in machine learning is assumed. The book covers How To:

Build 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 much more.
Apply some of the most common techniques to explore, visualize, encode, and preprocess behavioral data.
Use unsupervised learning algorithms to discover criminal behavioral patterns.
Program your own ensemble learning methods and use multi-view stacking to fuse signals from heterogeneous data sources.
Encode your data using different representations, such as feature vectors, time series, images, bags of words, graphs, and so on.
Train deep learning models with Keras and TensorFlow, including neural networks 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.
Train anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish trajectories.
And much more!

Welcome
Preface
	Supplemental Material
		Conventions
		Acknowledgments
Introduction
	What is Machine Learning?
	Types of Machine Learning
	Terminology
		Tables
		Variable Types
		Predictive Models
	Data Analysis Pipeline
	Evaluating Predictive Models
	Simple Classification Example
		K-fold Cross-Validation Example
	Simple Regression Example
	Underfitting and Overfitting
	Bias and Variance
	Summary
Predicting Behavior with Classification Models
	k-nearest Neighbors
		Indoor Location with Wi-Fi Signals
	Performance Metrics
		Confusion Matrix
	Decision Trees
		Activity Recognition with Smartphones
	Naive Bayes
		Activity Recognition with Naive Bayes
	Dynamic Time Warping
		Hand Gesture Recognition
	Dummy Models
		Most-frequent-class Classifier
		Uniform Classifier
		Frequency-based Classifier
		Other Dummy Classifiers
	Summary
Predicting Behavior with Ensemble Learning
	Bagging
		Activity recognition with Bagging
	Random Forest
	Stacked Generalization
	Multi-view Stacking for Home Tasks Recognition
	Summary
Exploring and Visualizing Behavioral Data
	Talking with Field Experts
	Summary Statistics
	Class Distributions
	User-Class Sparsity Matrix
	Boxplots
	Correlation Plots
		Interactive Correlation Plots
	Timeseries
		Interactive Timeseries
	Multidimensional Scaling (MDS)
	Heatmaps
	Automated EDA
	Summary
Preprocessing Behavioral Data
	Missing Values
		Imputation
	Smoothing
	Normalization
	Imbalanced Classes
		Random Oversampling
		SMOTE
	Information Injection
	One-hot Encoding
	Summary
Discovering Behaviors with Unsupervised Learning
	K-means clustering
		Grouping Student Responses
	The Silhouette Index
	Mining Association Rules
		Finding Rules for Criminal Behavior
	Summary
Encoding Behavioral Data
	Feature Vectors
	Timeseries
	Transactions
	Images
	Recurrence Plots
		Computing Recurence Plots
		Recurrence Plots of Hand Gestures
	Bag-of-Words
		BoW for Complex Activities.
	Graphs
		Complex Activities as Graphs
	Summary
Predicting Behavior with Deep Learning
	Introduction to Artificial Neural Networks
		Sigmoid and ReLU Units
		Assembling Units into Layers
		Deep Neural Networks
		Learning the Parameters
		Parameter Learning Example in R
		Stochastic Gradient Descent
	Keras and TensorFlow with R
		Keras Example
	Classification with Neural Networks
		Classification of Electromyography Signals
	Overfitting
		Early Stopping
		Dropout
	Fine-Tuning a Neural Network
	Convolutional Neural Networks
		Convolutions
		Pooling Operations
	CNNs with Keras
		Example 1
		Example 2
	Smiles Detection with a CNN
	Summary
Multi-User Validation
	Mixed Models
		Skeleton Action Recognition with Mixed Models
	User-Independent Models
	User-Dependent Models
	User-Adaptive Models
		Transfer Learning
		A User-Adaptive Model for Activity Recognition
	Summary
Detecting Abnormal Behaviors
	Isolation Forests
	Detecting Abnormal Fish Behaviors
		Explore and Visualize Trajectories
		Preprocessing and Feature Extraction
		Training the Model
		ROC curve and AUC
	Autoencoders
		Autoencoders for Anomaly Detection
	Summary
Setup Your Environment
	Installing the Datasets
	Installing the Examples Source Code
	Running Shiny Apps
	Installing Keras and TensorFlow
Datasets
	COMPLEX ACTIVITIES
	DEPRESJON
	ELECTROMYOGRAPHY
	FISH TRAJECTORIES
	HAND GESTURES
	HOME TASKS
	HOMICIDE REPORTS
	INDOOR LOCATION
	SHEEP GOATS
	SKELETON ACTIONS
	SMARTPHONE ACTIVITIES
	SMILES
	STUDENTS' MENTAL HEALTH
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