Patterns, Predictions, and Actions (eBook)

Foundations of Machine Learning

Moritz Hardt, Benjamin Recht (Autoren)

eBook Download: PDF

2022
320 Seiten
Princeton University Press (Verlag)
978-0-691-23372-7 (ISBN)

Infos

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

Produkt-Details

• Erscheint lt. Verlag: 23.8.2022
• Zusatzinfo: 41 b/w illus. 10 tables.
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
• Schlagworte: Accuracy and precision • activation function • Admissible set • affine transformation • algorithm • Analysis • Artificial Neural Network • belief propagation • Calculation • Capability • Causal graph • causal inference • Causality • causal model • causal reasoning • combination • Computation • conditional expectation • Condition number • Confidence interval • confounding • Control Theory • Covariance function • Cross-validation (statistics) • curve fitting • data set • Decision boundary • Decision-Making • Decision problem • Decision rule • Decision Theory • Decision Tree • Deep learning • de facto • Derivative • Descent direction • Detection Theory • Determinant • Dimensional Analysis • dot product • Dynamical system • Dynamic Programming • empirical risk minimization • Equation • Establishment Clause • estimation • Estimation theory • Estimator • Evaluation • existential quantification • Explanation • explanatory power • Exposition (narrative) • Extrapolation • feature vector • Function approximation • Function composition • Funding • Generative model • Gradient descent • Gradient Method • guessing • Heuristic • human subject research • idealization • implementation • Indicator function • inference • Initialization (programming) • Input/Output • Instance (computer science) • Instruction set • instrumental variable • interaction • interior point method • Interpolation • Intuition • Iteration • Iterative Method • Jacobian matrix and determinant • Joint probability distribution • Kalman Filter • learning • Learning rate • learning rule • Likelihood-ratio test • Loss Function • machine learning • Mathematical Optimization • Maxima and minima • Measurement • Meta-analysis • Model Predictive Control • network architecture • Notation • Observation • observational study • Odds Ratio • optimal control • optimization problem • Outcome (probability) • Parity function • pattern recognition • Perceptron • Planning • Planning horizon • Point Estimation • Policy • Pooling (resource management) • Prediction • predictive analytics • Predictive Modelling • Probability • Processing (programming language) • program manager • Q-function • Quantity • Rademacher complexity • Random Variable • Rate of Convergence • reason • Receiver Operating Characteristic • Reinforcement Learning • Representer theorem • result • risk assessment • sample complexity • Search problem • Selection rule • sensitivity and specificity • Sensor • sorting • Special case • State-transition matrix • Statistical classification • stochastic • stochastic approximation • Stochastic Gradient descent • Structural Equation Modeling • Summation • Supply Chain • Support Vector Machine • Symbolic Computation • System Identification • Test data • test design • Test set • theory • Tikhonov regularization • Treatment and control groups • Vaccine • Variable (computer science) • Word Embedding • Workflow
• ISBN-10: 0-691-23372-1 / 0691233721
• ISBN-13: 978-0-691-23372-7 / 9780691233727