chapter two

2 Types of machine learning

 

This chapter covers:

  • Three different types of machine learning: supervised, unsupervised, and reinforcement learning.
  • The difference between labelled and unlabelled data.
  • What is supervised learning and what is it useful for.
  • The difference between regression and classification, and how they are used.
  • What is unsupervised learning and what is it useful for.
  • What is reinforcement learning and what is it useful for.

As we learned in chapter 1, machine learning is common sense for a computer. Machine learning roughly mimics the process in which humans make decisions based on experience, by making decisions based on previous data. Naturally, programming computers to mimic the human thinking process is very challenging, as computers are engineered to store and process numbers, not to make decisions. This is the task that machine learning aims to tackle. Machine learning is divided into several branches depending on the type of decision to be made. In this chapter, we overview some of the most important among these branches.

Machine learning has applications in many fields. Can you help me think of some of these fields? Here is a list of some of my favorites:

2.1       What is the difference between labelled and unlabelled data?

2.1.1   Actually, what is data?

2.1.2   Ok. And what are features?

2.1.3   Labels?

2.1.4   Predictions

2.1.5   Labelled and unlabelled data

2.2       Supervised learning: The branch of machine learning that works with labelled data

2.2.1   Regression models predict numbers

2.2.2   Classification models predict a state

2.3       Unsupervised learning: The branch of machine learning that works with unlabelled data

2.3.1   Clustering algorithms split a dataset into similar groups

2.3.2   Dimensionality reduction simplifies data without losing too much information

2.3.3   Other ways of simplifying our data: Matrix factorization and singular value decomposition

2.3.4   Generative machine learning

2.4       What is reinforcement learning?

2.5       Summary

2.6       Exercises

2.6.1   Exercise 2.1

2.6.2   Exercise 2.2

2.6.3   Exercise 2.3