Machine learning refers to the automated identification of behavior patterns within datasets. Due to the complexity of the problems involved in these processes, traditional computer programs cannot deeply analyze data behavior. If we take human behavior as a reference, we know that many of our actions develop and improve based on experience. Machine learning focuses on designing algorithms that can “learn” and “adjust” over time based on acquired information.

In machine learning, we aim to develop algorithms capable of understanding patterns from data. Generally speaking, learning means transforming experience into knowledge. Training data serves as the initial experience, while the algorithm’s output represents the acquired knowledge. This knowledge is typically structured as a model that allows us to analyze and predict process behavior.

Three approaches to machine learning:

Supervised learning
In this method, data is structured in pairs that connect input and output. It is called supervised learning because the algorithm is guided by expected results while learning. An example would be predicting absenteeism in a company based on variables such as the day of the week, holidays, or job type. This type of learning is widely applied in areas such as fraud detection in financial transactions, demand forecasting, and failure prediction in industrial equipment.

Unsupervised learning
Unlike supervised learning, this approach does not establish direct input-output relationships. Instead, the algorithm’s goal is to identify complex patterns within large datasets. One example is clustering customers based on their purchasing behavior. Among its applications is clustering, a technique that groups objects based on specific characteristics, helping to uncover hidden structures and relationships in the data.

Reinforcement learning
This type of learning closely resembles human behavior. Here, the algorithm (or agent) learns by interacting with the environment and receiving rewards or penalties based on its decisions. For instance, in a scenario analyzing the relationship between price and sales of a product, the goal would be to determine the optimal price to maximize profitability. The agent adjusts its strategy based on market responses, applying stochastic control techniques to make decisions that optimize process performance