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.