Leseprobe
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Patterns, Predictions, and Actions
Benjamin Recht, Moritz Hardt
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Naturwissenschaften, Medizin, Informatik, Technik / Informatik, EDV
Beschreibung
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts
Patterns, 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 actions
- Pays special attention to societal impacts and fairness in decision making
- Traces the development of machine learning from its origins to today
- Features a novel chapter on machine learning benchmarks and datasets
- Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra
- An essential textbook for students and a guide for researchers
Kundenbewertungen
Selection rule, Summation, Instruction set, Function approximation, Predictive modelling, Processing (programming language), Activation function, Causal model, Function composition, Program Manager, Explanation, Policy, Planning horizon, Evaluation, Dynamic programming, Iterative method, Deep learning, Model predictive control, Test set, Confidence interval, Affine transformation, Jacobian matrix and determinant, Result, Admissible set, Estimator, Funding, Test data, Observation, Perceptron, Empirical risk minimization, Causal reasoning, Sorting, Instrumental variable, Sensitivity and specificity, Dimensional analysis, Measurement, Reinforcement learning, Machine learning, Causal inference, Pattern recognition, Risk assessment, Combination, Data set, Explanatory power, Supply chain, Loss function, Optimization problem, Instance (computer science), Random variable, System identification, Existential quantification, Causal graph, Estimation, Special case, Decision boundary, Sensor, Structural equation modeling, Probability, Interaction, Decision-making, Heuristic, Dynamical system, Establishment Clause, Gradient method, Prediction, Artificial neural network, Treatment and control groups, Meta-analysis, Learning, Exposition (narrative)