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Introduction: How to Use This Book?

  • Guy Lebanon
  • Mohamed El-Geish
Chapter
  • 2.6k Downloads

Abstract

Machine learning, data analysis, and artificial intelligence are becoming increasingly ubiquitous in our lives, and more central to the high-tech industry. These fields play a central role in many of the recent and upcoming revolutions in computing; for example, social networks, streaming video on demand, personal assistants (e.g., Alexa, Siri, and Google Assistant), and self-driving cars. Alphabet’s Executive Chairman, Eric Schmidt, went a step further at the 2016 Google Cloud Computing Conference in San Francisco when he said, “Machine learning and crowdsourcing data will be the basis and fundamentals of every successful huge IPO win in five years.”

Keywords

Upcoming Revolution Executive Chairman Self-driving Cars Gradient Boosting Decision Tree Script Example 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. G. Strang. Introduction to Linear Algebra. Wellesley Cambridge Press, fourth edition, 2009.Google Scholar
  2. W. Rudin. Principles of Mathematical Analysis. McGraw-Hill, third edition, 1976.Google Scholar
  3. W. F. Trench. Introduction to Real Analysis. Pearson, 2003.Google Scholar
  4. G. Thomas, M. D. Weir, and J. Hass. Thomas’ Calculus. Addison Wesley, twelfth edition, 2009.Google Scholar
  5. W. Feller. An Introduction to Probability Theory and its Application, volume 1. John Wiley and Sons, third edition, 1968.Google Scholar
  6. Sheldon M. Ross. Introduction to Probability Models. Academic Press, tenth edition, 2009.Google Scholar
  7. A. DasGupta. Fundamentals of Probability: A First Course. Springer, 2010.Google Scholar
  8. G. Casella and R. L. Berger. Statistical Inference. Duxbury, second edition, 2001.Google Scholar
  9. G. A. Seber and A. J. Lee. Linear Regression Analysis. Wiley Interscience, 2003.Google Scholar
  10. M. Kutner, C. Nachtsheim, J. Neter, and W. Li. Applied Linear Statistical Models. McGraw-Hill, fifth edition, 2004.Google Scholar
  11. B. Schölkopf and A. Smola. Learning with Kernels. MIT Press, 2002.Google Scholar
  12. C. D. Manning and H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.Google Scholar
  13. C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.Google Scholar
  14. K. P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Guy Lebanon
    • 1
  • Mohamed El-Geish
    • 2
  1. 1.AmazonMenlo ParkUSA
  2. 2.VoiceraSanta ClaraUSA

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