About the book
WHAT'S NEW IN THE 2ND EDITION
Comprehensive and in-depth coverage: The second edition offers a wide range of techniques for anomaly detection. It ensures a well-rounded understanding of the foundational concepts, making it suitable for beginners and seasoned professionals alike. Each chapter builds on the previous one, creating a cohesive and comprehensive learning experience.
Enhanced explanations and visual presentations: We’ve enriched this edition with a wealth of visualizations. These enhancements are designed to clarify complex concepts. You’ll learn not just the ‘how’ but also the ‘why’ behind each method.
Supervised and unsupervised learning techniques: We expand the second edition to cover the advanced machine learning techniques for anomaly detection. On the unsupervised learning part, you will be equipped with a wide range of modern techniques to discover new patterns. On the supervised learning part, you will gain a deeper understanding into grid-search, hyperparameter tuning, regularization, under-sampling and over-sampling techniques. You will use the code examples to build a suite of high-power machine learning models.
200+ data science Q&A: One of the standout features of the second edition is the inclusion of the Q&A sections in each chapter. By working through these questions, you’ll be well-prepared to showcase your expertise and secure your next job opportunity.
TABLE OF CONTENTS
Chapter 1: Introduction
Chapter 2: Histogram-Based Outlier Score (HBOS)
Chapter 3: Empirical Cumulative Distribution-based Outlier Detection (ECOD)
Chapter 4: Isolated Forest
Chapter 5: Principal Component Analysis (PCA)
Chapter 6: One-Class Support Vector Machine (OC-SVM)
Chapter 7: Gaussian Mixture Model (GMM)
Chapter 8: K-nearest Neighbors (KNNs)
Chapter 9: Local Outlier Factor (LOF)
Chapter 10: Cluster-Based Local Outlier Factor (CBLOF)
Chapter 11: Autoencoders
Chapter 12: Supervised Learning Primer
Chapter 13: Regularization
Chapter 14: Sampling Techniques for Extremely Imbalanced Data
Chapter 15: Representation Learning for Outlier Detection
Learn with Professor Chris Kuo
Chris Kuo is a seasoned data science professional and adjunct professor with over 25 years of experience applying advanced analytics across multiple industries. He has led high-impact data science initiatives in customer analytics, healthcare, fraud detection, and litigation support, and is the inventor of a U.S. patent in data-driven solutions. Throughout his career, he has held leadership roles at several Fortune 500 companies in the insurance and retail sectors.
In addition to his industry work, Chris Kuo also teaches at Columbia University and has previously taught at Boston University, the University of New Hampshire, and Liberty University, covering subjects such as time series forecasting, mathematical finance, economics, and management. He holds a Ph.D. in Economics from the State University of New York at Stony Brook and a B.S. in Nuclear Engineering from National Tsing Hua University in Taiwan. He currently resides in New York City with his wife.