Saleh R. Fundamentals of Robust Machine Learning...in Data Science 2025

Textbook in PDF format

An essential guide for tackling outliers and anomalies in Machine Learning and Data Science.
In recent years, Machine Learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust Machine Learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of Data Science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.
Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.
Fundamentals of Robust Machine Learning readers will also find
• A blend of robust statistics and machine learning principles
• Detailed discussion of a wide range of robust Machine Learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection
• Python code with immediate application to data science problems
Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in Data Science, Machine Learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.
Preface
Introduction
Robust Linear Regression
The Log-Cosh Loss Function
Outlier Detection, Metrics, and Standardization
Robustness of Penalty Estimators
Robust Regularized Models
Quantile Regression Using Log-Cosh
Robust Binary Classification
Neural Networks Using Log-Cosh
Multi-class Classification and Adam Optimization
Anomaly Detection and Evaluation Metrics
Case Studies in Data Science