Machine Learning in The Digital Humanities
INTRODUCTION
Machine learning (ML) is a subfield of computer science and mathematics that studies algorithms that improve their performance, or “learn” with experience and utilization of data. What is meant by “experience”, “learn” and “data” requires clarification and development. However, some alternative definitions provide context for the discussion.
In a web article for the MIT Sloan School of Management, Sara Brown writes: “Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems.”
Karen Hao, Senior AI Editor for the MIT Technology Review, provides another definition: “Machine-learning algorithms use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm.” She summarizes machine learning as follows: “Machine-learning algorithms find and apply patterns in data. And they pretty much run the world”
In the web journal Towards Data Science, machine learning engineer Gavin Edwards provides a definition that underscores the importance of data in machine learning: “Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyse it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.”
Machine learning has a connection to artificial intelligence and constitutes a very large area of study. For the purposes of motivating an investigation of machine learning in the context of the digital humanities, it suffices to note that machine learning algorithms are adaptive algorithms that improve their performance through the data on which they act in combination with specialized optimization (“learning”) approaches. Optimization is the basic paradigm of many machine learning algorithms. Machine learning algorithms use sample data, called training data, as their input to generate a mathematical model for prediction, classification, or decision making. The resulting model is not programmed to make these predictions, classifications, or decisions. These capabilities emerge from the model based only on the training data.
There are several classes, or models, of machine learning algorithms. The term “model”, used in this way, is not to be confused with the prediction models described above. Machine learning algorithms produce useful models to study and analyze va