This graduate-level machine learning textbook centers on the analysis and theory of algorithms. It provides a rigorous, broad introduction to machine learning designed for graduate students and researchers who want a solid theoretical foundation alongside practical insight into algorithm design and justification. The tone is focused, precise, and encouraging, with concise proofs and accessible explanations that help readers build confidence while tackling advanced topics.
Content is presented in largely self-contained chapters that begin from foundational ideas and move through key topics in modern machine learning. The material foregrounds the PAC learning framework and generalization bounds, then explores VC-dimension, Rademacher complexity, and core algorithms such as Support Vector Machines, kernel methods, boosting, and online learning. It covers multi-class classification, ranking, regression, algorithmic stability, dimensionality reduction, learning automata and languages, and reinforcement learning, always tying theory to potential applications. Each chapter ends with exercises, and the appendices provide essential probability background. The second edition adds three new chapters on model selection, maximum entropy models, and conditional entropy models, as well as expanded appendices on Fenchel duality, concentration inequalities, and information theory. More than half of the exercises are new in this edition.
- Core topics and algorithms: PAC learning, VC-dimension, Rademacher complexity, SVMs, kernel methods, boosting, online learning, multi-class classification, ranking, and regression.
- Learning frameworks and theory: generalization bounds, algorithmic stability, reinforcement learning, and dimensionality reduction.
- Learning through practice: end-of-chapter exercises reinforce concepts; chapters are largely self-contained for flexible study.
- Edition enhancements: three new chapters on model selection, maximum entropy models, conditional entropy models; expanded appendices on Fenchel duality, concentration inequalities, and information theory; more than half the exercises new.
- Clarity of presentation: concise proofs, clear explanations, and strong connections between theory and potential applications.
- Audience and use: ideal as a graduate course text and a rigorous reference for researchers and advanced practitioners.
Readers finish with a strong analytical toolkit for evaluating and designing machine learning algorithms, able to justify choices with solid theory and mathematics. The work builds confidence, fuels curiosity, and leaves a lasting impression of rigorous thinking applied to real-world data and research questions.
