Eigenvalue
/ˈaɪˌɡənˌvæl.juː/
noun … “the scale factor of a system’s intrinsic direction.”
Eigenvector
/ˈaɪˌɡənˌvɛk.tər/
noun … “the direction that refuses to bend under transformation.”
Covariance Matrix
/ˌkoʊ.vəˈriː.əns ˈmeɪ.trɪks/
noun … “a map of how variables wander together.”
Linear Algebra
/ˈlɪn.i.ər ˈæl.dʒə.brə/
noun … “the language of multidimensional space.”
Support Vector Machine
/səˈpɔːrt ˈvɛk.tər məˌʃiːn/
noun … “drawing the widest boundary that separates categories.”
Decision Tree
/dɪˈsɪʒ.ən triː/
noun … “branching logic that learns from examples.”
Decision Tree is a supervised machine learning model that predicts outcomes by recursively splitting a dataset into subsets based on feature values. Each internal node represents a decision on a feature, each branch represents the outcome of that decision, and each leaf node represents a predicted value or class. This structure allows the model to capture nonlinear relationships, interactions between features, and hierarchical decision processes in a transparent and interpretable way.
Gradient Descent
/ˈɡreɪ.di.ənt dɪˈsɛnt/
noun … “finding the lowest point by taking small, informed steps.”
Neural Network
/ˈnʊr.əl ˌnɛt.wɜːrk/
noun … “a computational web that learns by example.”
Linear Regression
/ˈlɪn.i.ər rɪˈɡrɛʃ.ən/
noun … “drawing the straightest line through messy data.”
Monte Carlo
/ˌmɒn.ti ˈkɑːr.loʊ/
noun … “using randomness as a measuring instrument rather than a nuisance.”
Monte Carlo refers to a broad class of computational methods that use repeated random sampling to estimate numerical results, explore complex systems, or approximate solutions that are analytically intractable. Instead of solving a problem directly with closed-form equations, Monte Carlo methods rely on probability, simulation, and aggregation, allowing insight to emerge from many randomized trials rather than a single deterministic calculation.