Stamp Mark. Introduction to Machine Learning with Applications in Information Security

2nd edition. — CRC Press, 2023. — 866 p. — (Chapman & Hall/CRC Machine Learning & Pattern Recognition). — ISBN: 978-1-003-26487-3.Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts. The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures is also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec. Finally, several cutting-edge deep learning topics are discussed, including dropout regularization, attention, explainability, and adversarial attacks.Probability sampling for estimating population parameters
Introduction to probability sampling.
Simple random sampling.
Stratified simple random sampling.
Systematic random sampling.
Cluster random sampling.
Two-stage cluster random sampling.
Sampling with probabilities proportional to the size.
Balanced and well-spread sampling.
Model-assisted estimation.
Two-phase random sampling.
Computing the required sample size.
Model-based optimization of probability sampling designs.
Sampling for estimating parameters of domains.
Repeated sample surveys for monitoring population parameters.
Sampling for mapping
Introduction to sampling for mapping.
Regular grid and spatial coverage sampling.
Covariate space coverage sampling.
Conditioned Latin hypercube sampling.
Spatial response surface sampling.
Introduction to kriging.
Model-based optimization of the grid spacing.
Model-based optimization of the sampling pattern.
Sampling for estimating the semivariogram.
Sampling for validation of maps.
Design-based, model-based, and model-assisted approach for sampling and inference.