Machine learning The new AI

"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 every day, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collec...

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Bibliographic Details
Main Author Alpaydin, Ethem
Format eBook Book
LanguageEnglish
Published Cambridge MIT Press 2016
The MIT Press
Edition1
SeriesMIT Press Essential knowledge series
Subjects
Algorithmus
Artificial intelligence
Computing and Processing
Datenanalyse
Datenschutz
Datensicherheit
Datenverarbeitung
Ethik
General Topics for Engineers
Informatik
Künstliche Intelligenz
Lernen
Machine learning
Neuronales Netz
Technologische Entwicklung
Wissenschaftsgeschichte
Zukunftsperspektive
Online AccessGet full text
ISBN9780262529518
0262529513

Cover

Table of Contents:
  • Intro -- 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 -- Connectionist Models in Cognitive Science -- Neural Networks as a Paradigm for Parallel Processing -- Hierarchical Representations in Multiple Layers -- Deep Learning -- 5 LEARNING CLUSTERS AND RECOMMENDATIONS -- Finding Groups in Data -- Recommendation Systems -- 6 LEARNING TO TAKE ACTIONS -- Reinforcement Learning -- Armed Bandit -- Temporal Difference Learning -- Reinforcement Learning Applications -- 7 WHERE DO WE GO FROM HERE? -- Make Them Smart, Make Them Learn -- High-Performance Computation -- Data Mining -- Data Privacy and Security -- Data Science -- Machine Learning, Artificial Intelligence, and the Future -- Closing Remarks -- NOTES -- GLOSSARY -- REFERENCES -- FURTHER READINGS
  • INDEX -- ETHEM ALPAYDIN