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 are
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. Most of the examples in
the book are drawn from the field of information security, with
many of the machine learning and deep learning applications focused
on malware. The applications presented serve to demystify the
topics by illustrating the use of various learning techniques in
straightforward scenarios. Some of the exercises in this book
require programming, and elementary computing concepts are assumed
in a few of the application sections. However, anyone with a modest
amount of computing experience should have no trouble with this
aspect of the book. Instructor resources, including PowerPoint
slides, lecture videos, and other relevant material are provided on
an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/.
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