Patterns, Predictions, and Actions

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A01=Benjamin Recht
A01=Moritz Hardt
Activation function
Admissible set
Affine transformation
Age Group_Uncategorized
Age Group_Uncategorized
Artificial neural network
Author_Benjamin Recht
Author_Moritz Hardt
automatic-update
Category1=Non-Fiction
Category=PBT
Category=UFM
Category=UYQM
Causal graph
Causal inference
Causal model
Causal reasoning
Combination
Confidence interval
COP=United States
Data set
Decision boundary
Decision-making
Deep learning
Delivery_Delivery within 10-20 working days
Dimensional analysis
Dynamic programming
Dynamical system
Empirical risk minimization
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
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Establishment Clause
Estimation
Estimator
Evaluation
Existential quantification
Explanation
Explanatory power
Exposition (narrative)
Function approximation
Function composition
Funding
Gradient method
Heuristic
Instance (computer science)
Instruction set
Instrumental variable
Interaction
Iterative method
Jacobian matrix and determinant
Language_English
Learning
Loss function
Machine learning
Measurement
Meta-analysis
Model predictive control
Observation
Optimization problem
PA=Available
Pattern recognition
Perceptron
Planning horizon
Policy
Prediction
Predictive modelling
Price_€50 to €100
Probability
Processing (programming language)
Program Manager
PS=Active
Random variable
Reinforcement learning
Result
Risk assessment
Selection rule
Sensitivity and specificity
Sensor
softlaunch
Sorting
Special case
Structural equation modeling
Summation
Supply chain
System identification
Test data
Test set
Treatment and control groups

Product details

  • ISBN 9780691233734
  • Weight: 721g
  • Dimensions: 178 x 254mm
  • Publication Date: 18 Oct 2022
  • Publisher: Princeton University Press
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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
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.

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