## #Basics

**Combinations**
{
⸢Math⸥
}
[
#Combinations
#Basics
]

**Eigenvalues and Eigenvectors**
{
⸢Math⸥
}
[
#Linear Algebra
#Basics
]

**Covariance Matrix**
{
⸢Math⸥
}
[
#Statistics
#Basics
]

## #Bayesian

**Bayes' Theorem**
{
⸢Math⸥
}
[
#Statistics
#Bayesian
]

## #Combinations

**Combinations**
{
⸢Math⸥
}
[
#Combinations
#Basics
]

## #Correlation

## #Data

**Gini Impurity**
{
⸢Machine Learning⸥
⸢Measurement⸥
}
[
#Data
]

**Information Gain**
{
⸢Machine Learning⸥
⸢Measurement⸥
}
[
#Data
]

## #Distance

**Cosine Similarity**
{
⸢Math⸥
}
[
#Set
#Distance
]

**Jaccard Similarity**
{
⸢Math⸥
}
[
#Set
#Distance
]

**Levenshtein Distance**
{
⸢Math⸥
}
[
#Distance
#NLP
]

**Mahalanobis Distance**
{
⸢Math⸥
}
[
#Distance
#Metric
]

## #Distributions

**Arcsine Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Bernoulli Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Beta Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Binomial Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Cauchy-Lorentz Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Gamma Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

## #Factorization

**Canonical Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]

**SVD: Singular Value Decomposition**
{
⸢Math⸥
}
[
#Matrix
#Factorization
#Linear Algebra
]

**Tucker Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]

## #Linear Algebra

**Eigenvalues and Eigenvectors**
{
⸢Math⸥
}
[
#Linear Algebra
#Basics
]

**Canonical Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]

**SVD: Singular Value Decomposition**
{
⸢Math⸥
}
[
#Matrix
#Factorization
#Linear Algebra
]

**Tucker Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]

**Diagnolize Matrice**
{
⸢Math⸥
}
[
#Linear Algebra
]

## #Matrix

## #Metric

**Mahalanobis Distance**
{
⸢Math⸥
}
[
#Distance
#Metric
]

## #NLP

**Term Frequency - Inverse Document Frequency**
{
⸢Math⸥
}
[
#NLP
]

**Levenshtein Distance**
{
⸢Math⸥
}
[
#Distance
#NLP
]

## #Poisson Process

## #Set

**Cosine Similarity**
{
⸢Math⸥
}
[
#Set
#Distance
]

**Jaccard Similarity**
{
⸢Math⸥
}
[
#Set
#Distance
]

## #Statistics

**Bayes' Theorem**
{
⸢Math⸥
}
[
#Statistics
#Bayesian
]

**Poisson Process**
{
⸢Statistics⸥
}
[
#Statistics
#Poisson Process
]

**Kendall Tau Correlation**
{
⸢Statistics⸥
}
[
#Statistics
#Correlation
]

**Covariance Matrix**
{
⸢Math⸥
}
[
#Statistics
#Basics
]

**Arcsine Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Bernoulli Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Beta Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Binomial Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Cauchy-Lorentz Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

**Gamma Distribution**
{
⸢Statistics⸥
}
[
#Statistics
#Distributions
]

## #Tensor

**Canonical Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]

**Modes and Slices of Tensors**
{
⸢Math⸥
}
[
#Tensor
]

**Tucker Decomposition**
{
⸢Math⸥
}
[
#Tensor
#Factorization
#Linear Algebra
]