data; local search algorithms, which are used in optimization problems; adversarial search algorithms, which are used in games and competitive environments where AI agents must act against opponents; and dynamic programming algorithms that break down problems into smaller, simpler subproblems to solve them recursively. Among these, we can list Depth-First Search [2] and Breadth-First Search [3], for traversing or searching tree or graph data structures; Alpha-Beta Pruning [4]; and Monte Carlo Tree Search [5], to name a few. * Optimization algorithms, including gradient descent and genetic programming, refine solutions, ensuring outcomes that align with specific goals, and are used for finding optimal solutions. These algorithms have extensive applications in AI-driven processes, ML model training, robotics, and data analysis. The subcategories of these are Linear Programming Algorithms used in optimization problems to maximize or minimize objective functions; optimization algorithms used in ML model training, parameter tuning, and AI model development; and constraint satisfaction problems that are used in scheduling, resource allocation, and automated planning to satisfy a set of constraints. Among these, we can list Genetic Algorithms [6], Ant Colony Optimization [7], Particle Swarm Optimization [8], and Bayesian Optimization [9], to name a few. * Supervised learning algorithms [10] enable machines to learn patterns and relationships from labeled data. These algorithms teach models how to map inputs to corresponding outputs and make accurate predictions and decisions based on past observations by training on input-output pairs. Among supervised learning algorithms, there are several subgroups, such as the following:
* Linear models are used for tasks requiring simple predictions such as regression and classification. They assume a linear relationship between input features and outputs; examples include Simple Linear Regression, Multiple Linear Regression, and Bayesian Regression. * Classification algorithms, such as K-Nearest Neighbors (KNN), Support Vector Machines (SVMs), and Decision Tree Algorithms, are used for tasks where the output is categorical. They assign data points to predefined classes or categories. Regularization techniques are used for preventing overfitting in machine learning models by penalizing complex models, such as Lasso and Ridge. Ensemble learning algorithms combine multiple ML models to improve performance. These methods are highly effective in reducing variance and bias, resulting in better generalization on unseen data, such as Bootstrap Aggregation, random forest, and AdaBoost. Generative models estimate the distribution of data, making them powerful for tasks such as classification and anomaly detection, such as Gaussian Discriminant Analysis (GDA), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMMs). Time Series Forecasting Algorithms are used for predicting future values based on historical data trends, for example, ARIMA and SARIMA. These techniques are widely used in financial forecasting, stock market predictions, and supply chain management. * Unsupervised learning algorithms [11] play a crucial role in tasks such as clustering, anomaly detection, and dimensionality reduction for uncovering hidden patterns in data without relying on labeled examples. They help machines to explore and understand the structure of large datasets and are mostly used in finance, healthcare, and e-commerce. Clustering algorithms group data points into distinct clusters based on similarity, such as through K-Means Clustering, Fuzzy C-Means (FCM) Clustering, and Gaussian Mixture Models (GMMs). They are used for market segmentation, customer profiling, and image recognition. Association rule mining algorithms, such as Z-Score, Local Outlier Factor (LOF), and Isolation Forest, identify interesting relationships between variables in large datasets. They are used in market analysis to understand purchase patterns.