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

Description

A concise overview of machine learning--computer programs that learn from data--the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of "the new AI." This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias.   Alpaydin, author of a popular textbook on machine learning, explains that as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data. Contents Series Foreword Preface 1: Why We Are Interested in Machine Learning The Power of the Digital Computers Store Data Computers Exchange Data Mobile Computing Social Data All That Data: The Dataquake Learning versus Programming Artificial Intelligence Understanding the Brain Pattern Recognition What We Talk about When We Talk about Learning History 2: Machine Learning, Statistics, and Data Analytics Learning to Estimate the Price of a Used Car Randomness and Probability Learning a General Model Model Selection Supervised Learning Learning a Sequence Credit Scoring Expert Systems Expected Values 3: Pattern Recognition Learning to Read Matching Model Granularity Generative Models Face Recognition Speech Recognition Natural Language Processing and Translation Combining Multiple Models Outlier Detection Dimensionality Reduction Decision Trees Active Learning Learning to Rank Bayesian Methods 4: Neural Networks and Deep Learning Artificial Neural Networks Neural Network Learning Algorithms What a Perceptron Can and Cannot Do Recurrent Networks for Learning Time Connectionist Models in Cognitive Science Neural Networks as a Paradigm for Parallel Processing Hierarchical Representations in Multiple Layers Deep Learning Learning Hidden Representations End-­to-­End Learning Generative Adversarial Networks 5: Learning Clusters and Recommendations Finding Groups in Data Recommendation Systems 6: Learning to Take Action Reinforcement Learning K-­Armed Bandit Temporal Difference Learning Learning to Play Games Reinforcement Learning in Real Life 7: Challenges and Risks The Other Side of Machine Learning Data Privacy and Security Biased Data Model Interpretability Ethical, Legal, and Other Social Aspects 8: Where Do We Go from Here? Make Them Smart, Make Them Learn High-­Performance Computation How Green Is My AI? Data Mining Data Science Machine Learning, Artificial Intelligence, and the Future Closing Remarks Glossary Notes Preface Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 7 Chapter 8 References Further Reading Index