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38638 results in Cambridge Textbooks

Page 597 of 1932

  • 11 - Money Stock Fluctuations

  • from Part II - Capital and Banking
  • Bruce Champ, Scott Freeman, Joseph H. Haslag, University of Missouri, Columbia
  • Book:
    Modeling Monetary Economies
    Published online:
    17 June 2022
    Print publication:
    02 June 2022, pp 199-217

  • Summary

    In this chapter, we develop a model economy in which people hold currency and deposits. When we look at data, we observe lots of movements in our measures of the monetary aggregates. Why is this so? By definition, the total money stock is the product of the monetary base and the money multiplier. Observable changes in the monetary aggregates that do not come from changes in the monetary base must result from changes in the money multiplier.

Machine Learning
  • A First Course for Engineers and Scientists
  • Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön
  • Published online:
    27 May 2022
    Print publication:
    31 March 2022
  • This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

The Science of Learning and Development in Education
  • A Research-based Approach to Educational Practice
  • Minkang Kim, Derek Sankey
  • Published online:
    26 May 2022
    Print publication:
    26 May 2022
  • All teachers need to know how children and adolescents learn and develop. Traditionally, this knowledge had been informed by a mix of speculative and scientific theory. However, in the past three decades there has been substantial growth in new scientific knowledge about how we learn. The Science of Learning and Development in Education provides an exciting and comprehensive introduction to this field. This innovative text introduces readers to brain science and the science of complex systems as it applies to human development. Section 1 examines the science of learning and development in the 21st century; Section 2 explores the emotional, cultural, moral and empathetic brain; and Section 3 focuses on learning, wellbeing and the ecology of learning environments. Written in an engaging style by leading experts and generously illustrated with colour photographs and diagrams, The Science of Learning and Development in Education is an essential resource for pre-service teachers.

Page 597 of 1932