Description: Statistical Learning Theory is a conceptual framework that seeks to understand how systems can learn from data. It is based on the idea that by observing patterns in data sets, it is possible to make inferences and predictions about new data. This theory combines principles of statistics, probability theory, and machine learning algorithms, providing a solid foundation for developing models that can generalize from examples. Key features include the ability to handle uncertainty, optimization of loss functions, and model validation through techniques like cross-validation. The relevance of this theory lies in its application across various fields, from artificial intelligence to economics, where the goal is to extract useful knowledge from large volumes of data. In the context of automated systems, Statistical Learning Theory becomes a fundamental pillar, as it allows for the automation of model selection and hyperparameter tuning, facilitating the creation of predictive models without the need for constant manual intervention.
