Bayes' theorem
Bayes' theorem (also called Bayes' rule or Bayes' law) is a fundamental theorem of probability theory that describes how to update the probability of a hypothesis given new evidence.
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Bayesian statistics
Bayesian statistics is a statistical paradigm in which probability expresses a degree of belief that is updated as evidence arrives, using Bayes' theorem.
Curse of Dimensionality
The curse of dimensionality is the set of problems that arise when data has a large number of features (dimensions): as dimensions increase, the volume of the space grows exponentially, the available data…
Distributionally Robust Optimization
Distributionally robust optimization (DRO) is a framework for making decisions when the probability distribution of uncertain inputs is not known exactly.
Earth Mover's Distance
Earth Mover's Distance (EMD), also known as the Wasserstein-1 distance, Kantorovich-Rubinstein metric, or Mallows's distance
Independently and Identically Distributed (i.i.d.)
Independently and identically distributed (abbreviated i.i.d., iid, or IID) data is a collection of random variables in which each variable has the same probability distribution as the others and all variables…
Information theory
Information theory is the mathematical study of the quantification, storage, and communication of information, founded by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication." Shannon…
KL Divergence (Kullback-Leibler Divergence)
Kullback-Leibler divergence, often abbreviated KL divergence and written $$D_{\mathrm{KL}}(P \parallel Q)$$
Log-Odds
Log-odds, also known as the logit, is a mathematical transformation that converts a probability value between 0 and 1 into a real number spanning from negative infinity to positive infinity.
Markov chain
A Markov chain is a stochastic process in which the probability of the next state depends only on the current state and not on the sequence of states that came before it.
Normal distribution
The normal distribution, also called the Gaussian distribution, is a continuous probability distribution defined by two parameters, a mean $$\mu$$ and a variance $$\sigma^2$$
Principal Component Analysis (PCA)
Principal component analysis (PCA) is an unsupervised learning technique for dimensionality reduction that identifies the orthogonal directions of maximum variance in high-dimensional data and projects…
Statistical learning theory
Statistical learning theory (SLT) is the mathematical framework that explains when and why machine learning algorithms generalize from a finite training sample to unseen data.
VC dimension
The Vapnik-Chervonenkis dimension, almost always shortened to VC dimension, measures the capacity of a class of binary classifiers by asking how many points the class can label in every possible way.