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Introduction to machine learning for bioinformatics
Machine learning is a subset of the broader field of artificial intelligence (AI). It enables systems to independently learn from data and execute tasks that they are not explicitly programmed to handle. Its goal is to give machines the ability to perform tasks that require human intelligence, such as diagnosing, planning, and predicting.
There are two main types of machine learning:
> Supervised learning relies on labeled datasets to teach algorithms an existing classification system and how to make predictions based on it. This ML type is used to train decision trees and neural networks.
> Unsupervised learning doesn’t use labels. Instead, algorithms try to uncover data patterns on their own. In other words, they learn things that we can’t teach them directly. This is comparable to how the human brain works.
It’s also possible to combine labeled and unlabeled data during training, which will result in semi-supervised learning. This ML type can be useful when you don’t have enough high-quality labeled data for a supervised learning approach, but you still want to use it to direct the learning process.
