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.”
Time Series
/ˈtaɪm ˌsɪər.iːz/
noun … “data that remembers when it happened.”
Time Series refers to a sequence of observations recorded in chronological order, where the timing of each data point is not incidental but essential to its meaning. Unlike ordinary datasets that can be shuffled without consequence, a time series derives its structure from order, spacing, and temporal dependency. The value at one moment is often influenced by what came before it, and understanding that dependency is the central challenge of time-series analysis.
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.
Principal Component Analysis
/ˈprɪn.sə.pəl kəˈpoʊ.nənt əˈnæl.ə.sɪs/
noun … “a way to rotate data until its most important structure faces you.”
Machine Learning
/məˈʃiːn ˌlɜːrnɪŋ/
noun … “teaching machines to improve by experience instead of explicit instruction.”
Machine Learning is a branch of computer science focused on building systems that can learn patterns from data and improve their performance over time without being explicitly programmed for every rule or scenario. Rather than encoding fixed logic, a machine learning system adjusts internal parameters based on observed examples, feedback, or outcomes, allowing it to generalize beyond the data it has already seen.
VAE
/ˌviː.eɪˈiː/
noun … “a probabilistic neural network that learns latent representations for generative modeling.”
Generative Pre-trained Transformer
/ˌdʒiːˌpiːˈtiː/
noun … “a generative language model that predicts and produces coherent text.”