Supervised learning is a fundamental paradigm in machine learning where an algorithm learns from a dataset containing input-output pairs, known as "labeled data." The goal is for the model to learn a mapping function that can predict the output variable (label) for new, unseen input data. The "supervision" comes from the labeled training data, which acts as a teacher, guiding the algorithm to produce correct outcomes. Common supervised learning tasks include classification (predicting a category) and regression (predicting a continuous value).

A. Segmentation is a task, often associated with clustering in unsupervised learning, that involves dividing a dataset into groups of similar items. It is not a learning paradigm defined by labeled data.

B. Generative AI is a category of AI focused on creating new content. While it can be trained using supervised methods, its defining characteristic is generation, not the specific training methodology.

C. Expert systems are a form of AI that relies on a pre-programmed knowledge base and a set of rules to solve problems, rather than learning from labeled data like machine learning models.

1. Russell

S. J.

& Norvig

P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson. In Chapter 19

"Learning from Examples

" the text defines supervised learning: "In supervised learning

the agent observes some example input–output pairs and learns a function that maps from input to output." (p. 694).

2. Stanford University. (2018). CS229 Machine Learning Course Notes. "In supervised learning

we are given a data set and already know what our correct output should look like

having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized into 'regression' and 'classification' problems." (p. 2).

3. Ayodele

T. O. (2010). Types of machine learning algorithms. In New Advances in Machine Learning. IntechOpen. This chapter distinguishes the major types

stating

"Supervised learning is the machine learning task of inferring a function from labeled training data." (Section 2.1

para. 1). DOI: https://doi.org/10.5772/9385