Machine Learning

Published in 1997, Tom M. Mitchell’s book Machine Learning is a fundamental introductory textbook to the field of machine learning. It’s written for advanced undergraduates and graduate students in computer science, and it requires no prior background in artificial intelligence or statistics. The book’s primary goal is to present the key algorithms and theoretical concepts that form the core of machine learning.
Mitchell defines machine learning as the study of algorithms that allow computers to improve their performance at a given task with experience. The book’s structure reflects this definition, introducing foundational concepts and then exploring a variety of learning paradigms.
Key Concepts and Structure
The book is organized into 13 chapters that cover a wide range of topics, including:
• Defining a Learning Problem: The book begins by establishing a clear framework for a “well-posed learning problem” which is defined by three components: a task (T), a performance measure (P), and a training experience (E). The goal of a machine learning algorithm is to improve its performance (P) on a task (T) by using a training experience (E).
• Supervised Learning: The book dedicates significant space to this learning paradigm, which involves training models on labeled data. Topics covered include decision tree learning, artificial neural networks, and Bayesian learning.
• Unsupervised and Reinforcement Learning: While the focus is heavily on supervised learning, the book also introduces other key areas of machine learning, such as instance-based learning and reinforcement learning, where an agent learns through trial and error.
• Theoretical Foundations: Mitchell delves into the theoretical underpinnings of the field, including computational learning theory and methods for evaluating hypotheses, which are crucial for understanding a model’s performance and limitations.
• Practical Applications: The book presents major algorithms and illustrates their use with examples, making the theoretical concepts more tangible. It also discusses the combination of different learning approaches, such as analytical learning and inductive learning.