Math fundamentals in AI:

- Linear Algebra: will teach you how to represent and manipulate data.
- Linear Algebra
- Linear Functions
- Linear Equation
- Non-Linear Equations

- Relational algebra

- Linear Algebra
**Calculus and optimization**, how to measure and improve performance.- Extrema
- Gradient (Calculus)
- Matrix Calculus
- Convexity
- Single and multivariate calculus

- Statistics:
**Statistics**, how to extract meaning from numerical data using data collection, analysis, interpretation and presentation of data.**Probability**, how to make decisions under uncertainty, by calculation of how likely an event may occur.

**Discreet Mathematics**- Graph Theory

Mastery of AI Mathematics allows:

- Intuitive model design
- intuitive understanding of model behaviors.

It’s also a requirement for those who wish to create new machine learning algorithms.

Concepts:

- Continuous variables: are variables that have an infinite number of values, such as speed and distance.
- Vectors: are used to represent numeric characteristics known as features in a mathematical and easily analyzable form.
- Dependent Variable(input value, explanatory variable, X): the variable that is measured and is affected by the independent variable.
- Independent Variable(response variable , target variable , Y): the variable that can manipulate or have a direct effect on the dependent variable.
- Nominal Variable: a type of the variable used to name, label, or categorize particular attributes that are being measured.
- Ordinal Variable: variables that have discrete values with some form of order involved.
- Predictor variable: the variable used to make a prediction for dependent variables.
- Monte Carlo method: a mathematical technique used to estimate the possible outcomes of an uncertain event by learning from experience(states, actions, and rewards).
- Multivariate analysis: the process of comparing and analyzing the dependency of multiple variables over one another.
- Regression: a technique used for investigating the relationship between independent variables or features and a dependent variable or outcome.
- Euclidian Distance: Eucladian Distance is the most used and standard measure for the distance between two points.
- Correlation Coefficient: IT is a measure of the strength(closeness) and direction of a linear association(linear relationship) between data points and is denoted by
. it is always calculated in the range of -1 to +1.- A correlation coefficient close to 0 is considered very weak and suggests little or no correlation.
- A correlation coefficient close to +1 or -1 is considered very strong and suggests high level of association.
- A correlation coefficient of +1 is considered perfect positive and suggests very high level of association.
- A correlation coefficient of -1 is considered perfect negative and suggests very high level of association.

Learning Material:

Interactive Graph

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