# Notation¶

The notation used throughout this book is summarized below.

## Linear Algebra¶

• $$x$$: A scalar

• $$\mathbf{x}$$: A vector

• $$\mathbf{X}$$: A matrix

• $$\mathsf{X}$$: A tensor

• $$\mathbf{I}$$: An identity matrix

• $$x_i$$, $$[\mathbf{x}]_i$$: The $$i^\mathrm{th}$$ element of vector $$\mathbf{x}$$

• $$x_{ij}$$, $$[\mathbf{X}]_{ij}$$: The element of matrix $$\mathbf{X}$$ at row $$i$$ and column $$j$$

## Set Theory¶

• $$\mathcal{X}$$: A set

• $$\mathbb{Z}$$: The set of integers

• $$\mathbb{R}$$: The set of real numbers

• $$\mathbb{R}^n$$: The set of $$n$$-dimensional vectors of real numbers

• $$\mathbb{R}^{a\times b}$$: The set of matrices of real numbers with $$a$$ rows and $$b$$ columns

• $$\mathcal{A}\cup\mathcal{B}$$: Union of sets $$\mathcal{A}$$ and $$\mathcal{B}$$

• $$\mathcal{A}\cap\mathcal{B}$$: Intersection of sets $$\mathcal{A}$$ and $$\mathcal{B}$$

• $$\mathcal{A}\setminus\mathcal{B}$$: Subtraction of set $$\mathcal{B}$$ from set $$\mathcal{A}$$

## Functions and Operators¶

• $$f(\cdot)$$: A function

• $$\log(\cdot)$$: The natural logarithm

• $$\exp(\cdot)$$: The exponential function

• $$\mathbf{1}_\mathcal{X}$$: The indicator function

• $$\mathbf{(\cdot)}^\top$$: Transpose of a vector or a matrix

• $$\mathbf{X}^{-1}$$: Inverse of matrix $$\mathbf{X}$$

• $$\odot$$: Hadamard (elementwise) product

• $$\lvert \mathcal{X} \rvert$$: Cardinality of set $$\mathcal{X}$$

• $$\|\cdot\|_p$$: $$\ell_p$$ norm

• $$\|\cdot\|$$: $$\ell_2$$ norm

• $$\langle \mathbf{x}, \mathbf{y} \rangle$$: Dot product of vectors $$\mathbf{x}$$ and $$\mathbf{y}$$

• $$\sum$$: Series addition

• $$\prod$$: Series multiplication

## Calculus¶

• $$\frac{dy}{dx}$$: Derivative of $$y$$ with respect to $$x$$

• $$\frac{\partial y}{\partial x}$$: Partial derivative of $$y$$ with respect to $$x$$

• $$\nabla_{\mathbf{x}} y$$: Gradient of $$y$$ with respect to $$\mathbf{x}$$

• $$\int_a^b f(x) \;dx$$: Definite integral of $$f$$ from $$a$$ to $$b$$ with respect to $$x$$

• $$\int f(x) \;dx$$: Indefinite integral of $$f$$ with respect to $$x$$

## Probability and Information Theory¶

• $$P(\cdot)$$: Probability distribution

• $$z \sim P$$: Random variable $$z$$ has probability distribution $$P$$

• $$P(X \mid Y)$$: Conditional probability of $$X \mid Y$$

• $$p(x)$$: probability density function

• $${E}_{x} [f(x)]$$: Expectation of $$f$$ with respect to $$x$$

• $$X \perp Y$$: Random variables $$X$$ and $$Y$$ are independent

• $$X \perp Y \mid Z$$: Random variables $$X$$ and $$Y$$ are conditionally independent given random variable $$Z$$

• $$\mathrm{Var}(X)$$: Variance of random variable $$X$$

• $$\sigma_X$$: Standard deviation of random variable $$X$$

• $$\mathrm{Cov}(X, Y)$$: Covariance of random variables $$X$$ and $$Y$$

• $$\rho(X, Y)$$: Correlation of random variables $$X$$ and $$Y$$

• $$H(X)$$: Entropy of random variable $$X$$

• $$D_{\mathrm{KL}}(P\|Q)$$: KL-divergence of distributions $$P$$ and $$Q$$

## Complexity¶

• $$\mathcal{O}$$: Big O notation

## Discussions¶ 