Introduction Bayesian Networks (BNs) are probabilistic graphical models that represent a set of variables and their conditional dependencies using a directed acyclic graph (DAG). These models provide a way of representing complex relationships in data through conditional probabilities. Bayesian Networks have been widely used in various fields such as artificial intelligence (AI), machine learning (ML),…
Introduction The K-Nearest Neighbors (KNN) algorithm is one of the simplest and most intuitive machine learning algorithms used for classification and regression tasks. It is a non-parametric method, meaning it makes no assumptions about the underlying data distribution. Instead, KNN classifies new data points based on the majority class (for classification) or the average of…
Introduction Stochastic Gradient Descent (SGD) is one of the most widely used optimization algorithms in machine learning, particularly for training large-scale models such as deep neural networks. SGD is an iterative method used to minimize a loss function by adjusting the model parameters in the direction of the negative gradient. This makes it an essential…