foundations of computational agents
Who so neglects learning in his youth, loses the past and is dead for the future.
– Euripides (484 BCE – 406 BCE), Phrixus, Frag. 927
From 2016 to 2020, the entire machine learning and data science industry has been dominated by two approaches: deep learning and gradient boosted trees. Specifically, gradient boosted trees is used for problems where structured data is available, whereas deep learning is used for perceptual problems such as image classification. …These are the two techniques you should be most familiar with in order to be successful in applied machine learning today.
– Chollet [2021, pp. 19,20]
Learning is the ability of an agent to improve its behavior based on experience. This could mean the following:
The range of behaviors is expanded; the agent can do more.
The accuracy on tasks is improved; the agent can do things better.
The speed is improved; the agent can do things faster.
The most common goal of machine learning is for an agent to understand the world using data. The aim is not to model the data, but to model the world that generates the data. Having better models of the world allows the agent to make better decisions and to carry out better actions.
Learning is an important aspect of acting intelligently. As Euripides pointed out, learning involves an agent remembering its past in a way that is useful for its future. Learning is one of the fundamental skills of an intelligent agent; however, it is usually not an end in itself. For example, a bank may learn from who has defaulted on a loan, in order to make decisions about who to give a loan to, but the bank may not want future decisions to be based purely on the inequities of the past. A self-driving car may learn to recognize people and faces in order to drive safely and recognize its owners, but the cost of being wrong – running over a person or opening for the wrong person – can be very high. For a smart watch predicting the activity, location, or health of a person, if it is just suggesting measuring activity or tracking a run, a good guess might be adequate; however, if it is calling an ambulance, it needs to be accurate and reliable.
This chapter considers general issues of learning and the problem of making a prediction as supervised learning: given a collection of training examples made up of input–output pairs, predict the output of a new example where only the inputs are given. What we call examples are sometimes called samples. The output – what is being predicted – is often called the target. Two predominant base algorithms from which other more sophisticated algorithms are built are presented. Section presents more sophisticated models based on these, including one of the dominant approaches in Chollet’s quote above. The other dominant approach is presented in the next chapter. Future chapters include other learning paradigms, as well as how to reason and make decisions with learned models.