Machine learning focuses on the development of algorithms and statistical models that can enable computers to learn from and make predictions or decisions without being explicitly programmed. Common sub-topics include supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Machine Learning usually requires linear algebra, calculus and proficiency in *Computer Programming*.

Neural networkUnsupervised learningHierarchical ClusteringK-means clusteringDimensionality ReductionDecision TreesMarkov Decision Process (MDP)Principal Component Analysis (PCA)Support Vector Machine (SVM)Graph neural network (GNN)Supervised learningBackpropagationValue iterationGradient descentLinear regressionExponential familyClusteringRegularization (mathematics)OverfittingKernel (operating system)Reinforcement learning (RL)BoostingLogistic Regression