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2275 results in Pattern Recognition and Machine Learning

Page 44 of 114

  • 1 - Introduction

  • Bernhard Mehlig, Göteborgs Universitet, Sweden
  • Book:
    Machine Learning with Neural Networks
    Published online:
    14 October 2021
    Print publication:
    28 October 2021, pp 1-12
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Computer Age Statistical Inference, Student Edition
  • Algorithms, Evidence, and Data Science
  • Bradley Efron, Trevor Hastie
  • Published online:
    26 October 2021
    Print publication:
    17 June 2021
  • The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

Machine Learning with Neural Networks
  • An Introduction for Scientists and Engineers
  • Bernhard Mehlig
  • Published online:
    14 October 2021
    Print publication:
    28 October 2021
  • This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.

  • 1 - Deep Learning on Graphs: An Introduction

  • Yao Ma, Michigan State University, Jiliang Tang, Michigan State University
  • Book:
    Deep Learning on Graphs
    Published online:
    02 September 2021
    Print publication:
    23 September 2021, pp 1-14
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Page 44 of 114