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Machine Learning

  • José Unpingco
Chapter

Abstract

Machine Learning is a huge and growing area. In this chapter, we cannot possibly even survey this area, but we can provide some context and some connections to probability and statistics that should make it easier to think about machine learning and how to apply these methods to real-world problems. The fundamental problem of statistics is basically the same as machine learning: given some data, how to make it actionable? For statistics, the answer is to construct analytic estimators using powerful theory. For machine learning, the answer is algorithmic prediction. Given a dataset, what forward-looking inferences can we draw? There is a subtle bit in this description: how can we know the future if all we have is data about the past? This is the crux of the matter for machine learning, as we will explore in the chapter.

References

  1. 1.
    L. Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer, Berlin, 2004)Google Scholar
  2. 2.
    V. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics (Springer, Berlin, 2000)Google Scholar
  3. 3.
    R.E. Schapire, Y. Freund, Boosting Foundations and Algorithms. Adaptive Computation and Machine Learning (MIT Press, Cambridge, 2012)Google Scholar
  4. 4.
    C. Bauckhage, Numpy/Scipy recipes for data science: Kernel least squares optimization (1) (2015). researchgate.netGoogle Scholar
  5. 5.
    W. Richert, Building Machine Learning Systems with Python (Packt Publishing Ltd., Birmingham, 2013)Google Scholar
  6. 6.
    E. Alpaydin, Introduction to Machine Learning (Wiley Press, New York, 2014)Google Scholar
  7. 7.
    H. Cuesta, Practical Data Analysis (Packt Publishing Ltd., Birmingham, 2013)Google Scholar
  8. 8.
    A.J. Izenman, Modern Multivariate Statistical Techniques, vol. 1 (Springer, Berlin, 2008)CrossRefGoogle Scholar
  9. 9.
    A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, vol. 46 (Wiley, New York, 2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • José Unpingco
    • 1
  1. 1.San DiegoUSA

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