Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Getting Started with Scientific Python Read chapter Read first chapter

2019 | OriginalPaper | Chapter

4. Machine Learning

Author : José Unpingco

Published in: Python for Probability, Statistics, and Machine Learning

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Statistics
next chapter Correction to: Probability
Footnotes
1
This assumes that the hypothesis set is big enough to capture the entire training set (which it is for this example). We will discuss this trade-off in greater generality shortly.
 
2
This is a slight generalization of the classic coupon collector problem.
 
3
We have also set the random seed to a fixed value to make the figures reproducible in the Jupyter Notebook corresponding to this section.
 
4
At least up to a rotation of the resulting orthonormal basis.
 
5
At least up to a rotation of the resulting orthonormal basis.
 
6
We discussed the geometry of high-dimensional space when we covered the curse of dimensionality in the statistics chapter.
 
7
Note that these entries are constructed from the data using an estimator of the covariance matrix because we do not have the full probability densities at hand.
 
8
Note that we are using the init=random keyword argument for this example in order to illustrate this.
 
Literature
1.
L. Wasserman, All of Statistics: A Concise Course in Statistical Inference (Springer, Berlin, 2004)
2.
V. Vapnik, The Nature of Statistical Learning Theory. Information Science and Statistics (Springer, Berlin, 2000)
3.
R.E. Schapire, Y. Freund, Boosting Foundations and Algorithms. Adaptive Computation and Machine Learning (MIT Press, Cambridge, 2012)
4.
C. Bauckhage, Numpy/Scipy recipes for data science: Kernel least squares optimization (1) (2015). researchgate.net
5.
W. Richert, Building Machine Learning Systems with Python (Packt Publishing Ltd., Birmingham, 2013)
6.
E. Alpaydin, Introduction to Machine Learning (Wiley Press, New York, 2014)
7.
H. Cuesta, Practical Data Analysis (Packt Publishing Ltd., Birmingham, 2013)
8.
A.J. Izenman, Modern Multivariate Statistical Techniques, vol. 1 (Springer, Berlin, 2008)CrossRef
9.
A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis, vol. 46 (Wiley, New York, 2004)
Metadata
Title
Machine Learning
Author
José Unpingco
Copyright Year
2019
Book
Python for Probability, Statistics, and Machine Learning

Print ISBN: 978-3-030-18544-2

Electronic ISBN: 978-3-030-18545-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-18545-9_4