Chapter 2 Introduction to Machine Learning

Machine learning can be applied to numerous areas of society, such as transportation, security, e-commerce and health care, to name just a few.

As defined by Grant and Wischik (2020),

“machine learning is the study of computer systems that use systematic mathematical procedures to find patterns in large datasets and that apply those patterns to make predictions about new situations.” 2


To paraphrase Judea Pearl, the 2011 Turing Award winner, machine learning is all about curve fitting3. To a certain extent, we can think of this as being an advanced version of simple linear regression, in which we aim to find a pattern or regularity within a large data set.

2.1 Machine Learning vs Artificial Intelligence

In order to conduct our own machine learning tasks, we will need to familiarise ourselves with some new terminology - please take a look over the details and definitions in the following sections.

Note: Some of the following details may be slightly simplified.

Before we proceed, we should make a clear distinction between machine learning (ML) and artificial intelligence (AI), as these can often be conflated.

  • Machine Learning is a mechanical, inductive process, and the ‘learning’ that takes place is strictly via the parameters specified by the human(s)4 writing the code - the machine itself does not exercise any independent thought or ‘intelligence’, nor does it question what it is learning, or why.

  • Artificial Intelligence, in contrast, can act (within the parameters defined during its creation) independently once created.

MLRobot5

References

Grant, Thomas D, and Damon J Wischik. 2020. On the Path to AI: Law’s Prophecies and the Conceptual Foundations of the Machine Learning Age. Cham: Springer International Publishing AG.

  1. Grant and Wischik (2020), p.x↩︎

  2. Grant and Wischik (2020), p.41↩︎

  3. or possibly AI↩︎

  4. “Artificial Intelligence & AI & Machine Learning” by mikemacmarketing is licensed under CC BY 2.0 ↩︎