6.2 Machine learning as programming paradigm

  • ML reflects a different programming paradigm (Chollet and Allaire 2018, Chap. 1.1.2)
  • Machine learning arises from this question: could a computer […] learn on its own how to perform a specified task? […] Rather than programmers crafting data-processing rules by hand, could a computer automatically learn these rules by looking at data?
Source: Chollet and Allaire (2018) , Chap. 1.1.2
  • Classical programming (paradigm of symbolic AI)
    • Humans input rules (a program) and data to be processed according to these rules, and out come answers
  • Machine learning paradigm
    • Humans input data + answers expected from the data, and out come the rules [these rules can then be applied to new data]
  • ML system is trained rather than explicitly programmed
    • Trained: Presented with many examples relevant to a task &rarr finds statistical structure in these examples &rarr allows system to come up with rules for automating the task (remember Alpha Go)
  • Role of math
    • While related to math. statistics, ML tends to deal with large, complex datasets (e.g., millions of images, each consisting of thousands of pixels)
    • As a result ML (especially deep learning) exhibits comparatively little mathematical theory and is engineering oriented (ideas proven more often empirically than mathematically) (Chollet and Allaire 2018, Chap. 1.1.2)


Chollet, Francois, and J J Allaire. 2018. Deep Learning with R. 1st ed. Manning Publications.