Before any application sees widespread use, its artificial intelligence (AI) has to learn the tools of its trade. Think of it as the AI going to school, with the developer as its teacher. With instruction and experience, the AI will become self-sufficient, responding to data without further input from human programmers. This is machine learning in a nutshell.
While the industry uses dozens of machine learning algorithms, they generally fall under one of four types. Here’s a comprehensive look into the four types of machine learning algorithms.
- Supervised Learning
This type is essentially classroom instruction for AI, teaching it through example. The developer provides data, while the algorithm must show its answer and how it arrived at such a conclusion. The developer then makes adjustments to the algorithm based on the latter’s answers and repeats the process until the algorithm gets it mostly right.
Two of the most common classifications of algorithms that learn through supervised learning are classification and regression.
- Classification involves the algorithm classifying or predicting the correct category label for the data provided. One prominent example is the spam filter in emails, sorting emails into ‘spam’ or ‘not spam’ based on observational data.
- Regression requires the algorithm to make sense of the relationships among the provided data. Regression is standard among applications that forecast economic trends like real estate prices and stock prices.
Supervised learning is the only type of machine learning that uses continuous target variables, particularly for regression-type algorithms like forest regression notebook source. Meanwhile, classification-type ones use categorical target variables, which are also present in other types of machine learning.
- Unsupervised Learning
Unsupervised learning is the complete opposite. Without a human teacher to instruct and make adjustments, the AI can only rely on receiving unlabeled data. Developers have valid reasons for employing this approach, one being that they may not have a clear idea of how to deal with the data. The more data the algorithm processes, the more accurate its answers become.
This type works best for applications that hinge on data grouping and pattern recognition. Two approaches unsupervised learning algorithms may employ include association and clustering.
- In association (also known as association rule mining), the algorithm runs based on its if-then script. It observes patterns and associations based on how the data fulfill the if-then conditions. Association-type algorithms are common in market basket analysis.
- Clustering is basically classification but with unlabeled data. This approach is ideal for studying anomalies in datasets, making them a favorite in market segmentation, social network analysis, and medical imaging.
While more complicated and costly than supervised learning, unsupervised learning results in some of the most sophisticated algorithms. It isn’t unusual for one that’s developed this way to teach its human creators something new, if not groundbreaking.
- Semi-Supervised Learning
This type exists in the middle ground between supervised and unsupervised learning, created to mitigate the downsides of the two. It uses both labeled and unlabeled data, though it often uses more of the latter. An algorithm developed via semi-supervised learning can label unlabeled data when adequately programmed, opening more possibilities.
Semi-supervised learning employs classification and clustering. One example of an algorithm programmed via classification is Google’s search engine algorithm, which recently received several new updates to classify quality content more accurately.
Meanwhile, clustering has seen use in medical science, namely in classifying DNA sequences. Its contribution to bioinformatics can help doctors better understand health conditions that were previously a mystery.
- Reinforcement Learning
Reinforcement learning is supervised learning but with a few differences. While a human teacher is present, they don’t possess the answer to a presented problem. This setup forces the algorithm to solve through trial and error, making decisions along the way.
If the AI makes the right decisions, the teacher reinforces that behavior. The reinforcement can either be positive or negative. Positive reinforcement urges the algorithm to continue making a decision that triggers a particular event. Meanwhile, negative reinforcement encourages it to go by a decision that prevents an event from happening.
Reinforcement learning can develop algorithms via classification or control depending on the presence of target variables. Algorithms that receive labeled data employ classification, which is best for optimizing marketing campaigns and strategies. On the other hand, those that work with unlabeled data use control, suitable in the realm of robotics.
Conclusion
Algorithms may increase in number as new technologies come online. However, they’ll most likely fall under one of these four types depending on the industry or sector they aim to serve. Choosing the proper form of learning can make a difference for any algorithm.
