## 3 Artificial Intelligence (AI) and Machine Learning (ML)
### Machine Learning (ML)  
ML, on the other hand, is a subset of AI that is focused on the creation and refinement of algorithms and statistical models that can effectively learn from historical data and thus accomplish tasks (such as making decisions or predictions) without explicit instructions/programming tailored to the specific task at hand [19; 22]. The fundamental  
Figure 1: Artificial intelligence vs. machine learning. principles and methodologies of ML include supervised learning, unsupervised learning, and reinforcement learning [31; 32]. Supervised learning: In supervised learning, models are trained using labeled data, where each input datum point is associated with a specific target label. During the training, the algorithm establishes the relationship between the input and output by modifying its internal parameters to reduce the discrepancy between the predicted output and the real output, known as the error or loss. The trained algorithm can thus be utilized for predicting or categorizing new unseen data [31; 32; 33]. Supervised learning can be further classified into two primary types: classification and regression [34]. Classification involves predicting the category or class label of future observations using past observations with known labels. The output variable is categorical, indicating that it assumes a finite set of values. Whereas regression problems involve predicting a continuous numerical value using input features. The output/response variable is continuous, allowing it to assume any value within a specific range. Some of the underlying algorithms of supervised learning include linear regression, support vector machine (SVM), logistic regression, ANN, gradient-boosted regression/classification, random forest, etc. [34; 35; 36; 37]. Unsupervised learning: Unsupervised learning involves models identifying patterns in data without the need for labeled examples or explicit guidance [38]. The goal is to investigate the inherent organization of data and decipher concealed distributional patterns, clusters, or connections in the data without predetermined labels or intended results [38]. Unsupervised learning is especially beneficial in situations where there is a lack of labeled data, when obtaining such data is costly, or when the data's underlying structure is not fully comprehended. Tasks, methods, and approaches associated with unsupervised learning include clustering methods, dimensionality reduction strategies, anomaly detection, association rule learning algorithms, etc. [38; 39; 40]. Clustering methods seek to categorize related data points into groups or clusters using a similarity measure. The goal is to divide the data into separate groups where data points in the same group are more alike to each other than to those in other groups. Clustering algorithms such as hierarchical clustering and K-means clustering are commonly used [38; 39]. Dimensionality reduction strategies focus on decreasing the number of features, characteristics, or variables in the data while retaining critical data information. This can aid in visualizing data with many dimensions, eliminating interference, and expediting further processing. Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are popular unsupervised ML techniques used for dimensionality reduction and data exploration [41; 42]. Anomaly identification, sometimes referred to as outlier detection, is the process of discovering uncommon or unusual patterns or data points that differ significantly from the typical data distribution [43]. Anomaly detection techniques encompass distance-based methods, density-based techniques, isolation forests, etc. Reinforcement learning: This category of ML entails models learning through interactions with an environment and obtaining feedback, in the form of incentives or punishments [44; 45; 46].