Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

This volume in the MIT Press Essential Knowledge series provides a concise and accessible introduction to machine learning, the technology driving modern artificial intelligence. Written by Ethem Alpaydin, the book explains how computer programs can learn from data to solve complex problems without requiring explicit programming for every rule. It traces the evolution of machine learning from its theoretical roots to its current dominance in applications ranging from voice recognition and product recommendations to autonomous vehicles. Alpaydin emphasizes that as Big Data has expanded, the algorithms used to process this information into actionable knowledge have advanced significantly. The text covers fundamental concepts such as pattern recognition, artificial neural networks inspired by the human brain, and reinforcement learning. It also explores algorithms designed to identify associations between data instances. Importantly, this expanded edition addresses critical contemporary challenges facing the field, including issues of privacy, security, accountability, and algorithmic bias. The author discusses the necessity for transparency and explainability in AI systems, as well as the ethical and legal implications of relying on data-based decision-making in society. Designed for a general audience, this primer requires no prior in-depth knowledge of mathematics or computer programming, making it suitable for everyday readers and classroom use. By offering a clear overview of "the new AI," Alpaydin demystifies the technology that increasingly underpins our daily lives. The book serves as a comprehensive guide to understanding how machine learning works, its practical applications, and the significant societal questions raised by the widespread adoption of intelligent systems.