6.11 What is an ML algorithm?

  • As summarized by Chollet and Allaire (2018 Chap. 1.1.2)
  • Machine learning arises from this question: could a computer go beyond “whatever we know how to order it to perform” and learn on its own how to perform a specified task? Could a computer surprise us? 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

Source: Chollet and Allaire (2018) , Chap. 1.1.2

  • New programming paradigm
    • 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: humans input data + answers expected from the data, and out come the rules. These rules can then be applied to new data to produce original answers.
  • A machine-learning system is trained rather than explicitly programmed
    • It is presented with many examples relevant to a task, and it finds statistical structure in these examples that eventually allows the system to come up with rules for automating the task. For instance, if you wished to automate the task of tagging your vacation pictures, you could present a machine-learning system with many examples of pictures already tagged by humans, and the system would learn statistical rules for associating specific pictures to specific tags.
  • “Machine learning is tightly related to mathematical statistics, but it differs from statistics in several important ways. Unlike statistics, machine learning tends to deal with large, complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would be impractical. As a result, machine learning, and especially deep learning, exhibits comparatively little mathematical theory—maybe too little—and is engineering oriented. It’s a hands-on discipline in which ideas are proven empirically more often than theoretically.” (Chollet and Allaire 2018, Chap. 1.1.2)

References

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