Over the past decade, the field of Machine Learning (ML) has attracted its fair share of attention with remarkable breakthroughs in areas such as self-driving cars, drug discovery, and chatbots like the recently acclaimed ChatGPT. Many view ML models as black box entities, however an understanding of what happens under the hood is crucial for those looking to get involved in the field. As a first step, it is important to recognize that ML techniques don’t come in a one size fits all fashion. There are different types of ML based on the nature of the problem and the type of data used. In this article, we’ll explore the three main categories: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
Supervised Learning
Supervised learning is a type of learning that uses labeled data (i.e. example inputs and their desired outputs) to learn a general rule that maps inputs to outputs. For instance, suppose we want to detect pictures of dogs. To apply a supervised learning method to this task, we first gather a labeled dataset of images (i.e. images of dogs labeled ‘dog’ and images of things other than dogs labeled ‘not dog’ ). Next, we train a model on this dataset through which it learns to map dog features to label ‘dog’ and likewise none dog features to label ‘not dog’. The same framework can be applied to other tasks such as speech recognition, text classification, fraud detection among countless others. Thus, supervised learning allows for a wide range of applications; however a significant downside is that it requires labeled data which is not always readily available.
Unsupervised Learning
Unsupervised learning is a type of learning that deals with unlabeled data (i.e. data which has no predefined categories or outcomes). Instead, these methods attempt to discover patterns and relationships within the data. For example, suppose we have collected data on customers and would like to define customer segments. We can leverage an unsupervised learning technique such as clustering to identify outstanding categories of customers with similar features. Thus, unsupervised learning is more so useful for exploratory analysis. Such techniques are especially nice because they don’t require labeled data which is often hard to come by. However, by nature it is difficult to evaluate their performance.
Reinforcement Learning
Reinforcement learning is a type of learning that focuses on decision-making policies within settings where an agent learns to make optimal decisions by interacting with its environment. Often, the agent receives feedback in the form of rewards or penalties based on its actions, which it uses to improve its decision-making policy. For instance, suppose we want to train an agent to play the game ‘Doodle Jump’. At each time segment, our agent takes an action (e.g. jump to the left). If the action leads to failure, the agent incurs a penalty. Within this reinforcement learning setup the agent will try to minimize penalties, learning how to best play the game in the process. This form of learning is effective for navigating complex environments where an optimal solution is hard to find. However, it makes use of trial and error which generally makes it a computationally expensive learning strategy.
Machine learning has revolutionized many industries and has the potential to transform many more. Understanding the different types of machine learning and their applications is essential for building effective machine learning systems. In this article, we have explored the three main types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each type has its advantages and disadvantages, and the choice of the type depends on the nature of the problem and the type of data available. As machine learning continues to evolve, we can expect to see new types of machine learning emerge that can handle even more complex problems and lead to even more groundbreaking discoveries.
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