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Major types of machine learning
In this article, we introduce three major machine learning types, i.e., supervised learning, unsupervised learning and reinforcement learning.
In this article, we introduce the major categories of machine learning.
- Supervised Learning: it aims to learn from a training set containing labelled data (that is, containing both inputs and outputs), such that it can be well applied to the unseen data. Starting from the analysis of a given training dataset, which is a sequence of input-output pairs, the supervised learning algorithm produces an inferred function to make predictions about the output values.
- Unsupervised Learning: it aims to infer structural characteristics (typically hidden) from unlabelled data.
- Reinforcement Learning: it aims to identify optimal actions to interact with an environment by maximising corresponding rewards. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context based on the feedback loop from the environment.
The following table (Table 1) provides a summary of the main categories of machine learning and the corresponding examples of financial applications.
| Categories | Examples |
|---|---|
| Supervised Learning | |
| (bullet) Regression | Forecast returns |
| (bullet) Classification | Predict the direction of returns |
| Unsupervised Learning | |
| Clustering | Identify the most common signs of market stress. |
| Reinforcement Learning | |
| Reinforcement Learning | Learn trading strategy |
Table 1: Categories of machine learning and financial application examples.
This article is from the online course:
An Introduction to Machine Learning in Quantitative Finance
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An Introduction to Machine Learning in Quantitative Finance
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