The Different Types of Machine Learning

Uwais Iqbal β€’ 2022-11-02

There are different approaches to Machine Learning that enable learning in a variety of circumstances and situations.

The different algorithms and approaches in Machine Learning can be summarised in three broad types:

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Supervised Learning

Supervised Learning is a type of Machine Learning where a model is trained on a specific task. A typical example of a supervised learning task is classifying spam emails.

The model is supervised meaning that it is given lots of examples of emails along with information about whether the email is spam or not. From the labelled data, the model can learn what spam emails look like so it can then successfully detect and classify spam emails.

There is a barrier to entry in order to use supervised learning since it requires a training dataset that is labelled by humans.

Unsupervised Learning

Unsupervised Learning is a type of Machine Learning where the model is completely data-driven. A typical example of an unsupervised learning task is automatically clustering documents. The model is unsupervised so it learns how to cluster based on patterns that exist within the data itself.

Unsupervised learning is attractive for many applications because of the low barrier to entry. All that is needed is the raw dataset. It doesn’t require data to be labelled by humans.

Reinforcement Learning

Reinforcement Learning is a type of ML where the model is trained to learn from mistakes in a simulated environment. Reinforcement learning is typically used in robotics and manufacturing applications.

The model learns to perform a task by reinforcement; positive outcomes are rewarded and negative outcomes are punished. It is quite similar to how pets are trained to perform particular actions by reinforcing correct behaviours with rewards.

Machine Learning in Legal AI

Supervised learning is particularly important since it is a way of capturing expertise that can then be scaled using a model. Within Legal AI, there are many specific tasks that require particular expertise. For example, extracting fields from a set of contracts is a specific task that can be taught to a model using supervised learning.

Supervised learning could be used to train Legal AI models to learn how to perform these specific tasks and legal expertise could be scaled so the burden of manual work is considerably reduced.

Unsupervised learning is also very useful in Legal AI use cases. Without requiring labelled data, the model can learn to spot patterns in the data. A Legal AI use case where unsupervised learning can be used is in clustering documents as part of a eDiscovery use case.