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Chapter VI. Machine Learning
VI.1. Introduction
Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that enables machines to
learn from data without explicit programming, similar to how humans learn. Its objective is to
develop models capable of solving problems by identifying underlying correlations within data,
thereby facilitating accurate predictions. Through ML, data processing enables the identification
of correlations across diverse aspects, enabling the system to anticipate their likely
consequences. The utilization of machine learning techniques has gained significant traction
across various engineering domains, including coastal engineering.
VI.2. Machine Learning
The steps below represent the general ones for creating a machine learning model. The workflow
is illustrated in the Figure VI-1.
1. Definition of the problem.
2. Data Gathering.
3. Data Preparation.
4. Model Selection.
5. Model Training.
6. Model evaluation.
7. Model Deployment
8. Model monitoring and maintenance.
Figure VI-1 Machine Learning workflow diagram