# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 1 Introduction
### **Supervised Learning (SL)**  
SL is a fundamental paradigm in ML, where the objective is to predict the label or value of new instances based on a training dataset with known labels. The central idea is to learn a mapping from input features to output labels, utilizing a labeled dataset to train the model, as shown in Figure 2. This process involves the development of an inference function that can generalize from the training data to make accurate predictions on unseen data. SL encompasses two main types of problems: classification and regression.  
* Classification: When the output variable is categorical, the problem is known as classification. The goal is to assign an input to one of several predefined classes. This can involve binary classification, where there are only two classes, or multiclass classification, where there are more than two classes. To address these issues, commonly employed classification algorithms include logistic regression, support vector machines, decision trees, and neural networks. Classification model performance is frequently assessed using metrics such as accuracy, precision, recall, and the F1 score [21].
* Regression: When the output variable is continuous, the problem is referred to as regression. The objective in this context is to forecast a continuous value from the input features. Frequently used regression algorithms include linear regression, ridge regression, lasso regression, least absolute deviations (LAD), partial least squares (PLS), and more sophisticated models like random forests and gradient boosting machines. The effectiveness of regression models is generally evaluated using metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared (R\({}^{2}\)) [22].  
Figure 1: Hierarchy between AI, ML, and DL.  
In supervised learning, the inference function is obtained by minimizing a loss function that quantifies the difference between predicted outputs and actual labels in the training set. This loss function is adapted to the specific problem type. For classification tasks, commonly used loss functions are cross-entropy loss and hinge loss, while for regression tasks, MSE is often employed. The choice of loss function influences the optimization process and the resulting model's performance [23].  
The training process consists of modifying the model parameters to reduce the loss function on the training data. This is generally accomplished using optimization algorithms like gradient descent and its variations (e.g., stochastic gradient descent, Adam). The optimization method iteratively adjusts the model parameters in a direction that decreases the loss, enabling the model to capture the data's underlying patterns [18].  
A critical challenge in supervised learning is ensuring that the model generalizes well to unseen data. Overfitting occurs when a model learns the noise and details in the training data to the extent that it performs poorly on new data. Techniques such as cross-validation, regularization (e.g., L1 and L2 regularization), and early stopping are employed to mitigate overfitting and enhance generalization [15].  
This ML method is a widely used approach in machine learning, enabling the prediction of labels or values for new instances based on historical data. By developing robust inference functions, carefully selecting algorithms, and conducting rigorous evaluations, supervised learning models can achieve high accuracy and generalization, making them indispensable tools across various fields.