Patterns, Predictions, and Actions

Foundations of Machine Learning

Inbunden, Engelska, 2022

571 kr

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Beskrivning

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers

Produktinformation

Utgivningsdatum:2022-10-18
Mått:178 x 254 x 27 mm
Vikt:721 g
Format:Inbunden
Språk:Engelska
Antal sidor:320
Förlag:Princeton University Press
ISBN:9780691233734

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik
Affärsapplikationer inom Data och IT

Mer om författaren

Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems. Benjamin Recht is professor of electrical engineering and computer sciences at the University of California, Berkeley.

Recensioner i media

"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability."---Jonathan Shock, Mathemafrica

Innehållsförteckning

List of FiguresList of TablesPrefaceAcknowledgments1 IntroductionAmbitions of the twentieth centuryPattern classiﬁcationPrediction and actionChapter notes2 Fundamentals of PredictionModeling knowledgePrediction via optimizationTypes of errors and successesThe Neyman-Pearson LemmaDecisions that discriminateChapter notes3 Supervised LearningSample versus populationSupervised learningA ﬁrst learning algorithm: The perceptronConnection to empirical risk minimizationFormal guarantees for the perceptronChapter notes4 Representations and FeaturesMeasurementQuantizationTemplate matchingSummarization and histogramsNonlinear predictorsChapter notes5 OptimizationOptimization basicsGradient descentApplications to empirical risk minimizationInsights from quadratic functionsStochastic gradient descentAnalysis of the stochastic gradient methodImplicit convexityRegularizationSquared loss methods and other optimization toolsChapter notes6 GeneralizationGeneralization gapOverparameterization: Empirical phenomenaTheories of generalizationAlgorithmic stabilityModel complexity and uniform convergenceGeneralization from algorithmsLooking aheadChapter notes7 Deep LearningDeep models and feature representationOptimization of deep netsVanishing gradientsGeneralization in deep learningChapter notes8 DatasetsThe scientiﬁc basis of machine learning benchmarksA tour of datasets in diﬀerent domainsLongevity of benchmarksHarms associated with dataToward better data practicesLimits of data and predictionChapter notes9 CausalityThe limitations of observationCausal modelsCausal graphsInterventions and causal eﬀectsConfoundingExperimentation, randomization, potential outcomesCounterfactualsChapter notes10 Causal Inference in PracticeDesign and inferenceThe observational basics: Adjustment and controlsReductions to model ﬁttingQuasi-experimentsLimitations of causal inference in practiceChapter notes11 Sequential Decision Making and Dynamic ProgrammingFrom predictions to actionsDynamical systemsOptimal sequential decision makingDynamic programmingComputationPartial observation and the separation heuristicChapter notes12 Reinforcement LearningExploration-exploitation trade-oﬀs: Regret and PAC-errorUnknown models and approximate dynamic programmingCertainty equivalence is often optimalThe limits of learning in feedback loopsChapter notes13 EpilogueBeyond pattern classiﬁcation?14 Mathematical BackgroundCommon notationMultivariable calculus and linear algebraProbabilityEstimationBibliographyIndex