Foundations of Machine Learning & AI

Artificial Intelligence encompasses computational systems capable of performing tasks typically requiring human intelligence, formalized at the 1956 Dartmouth Summer Research Project organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. Machine learning (ML) is broadly categorized into supervised learning (training on labeled datasets for classification and regression), unsupervised learning (identifying latent patterns, clustering, and dimensionality reduction like PCA), and reinforcement learning (optimizing agent actions via reward signals in Markov Decision Processes). Fundamental algorithms include linear regression, logistic regression, support vector machines (SVM), decision trees, and random forests, evaluated using statistical metrics such as accuracy, precision, recall, and F1-score.