Introduction to Probability for Machine Learning

Study Notes

Systematic Study of Uncertainty

Involves analyzing unpredictable outcomes and making informed decisions based on incomplete information.
Utilizes statistical methods to assess risks and uncertainties in various fields such as finance, science, and everyday life.

Definition of Probability

A measure of the likelihood of an event occurring, expressed as a number between 0 (impossible) and 1 (certain).
Can be calculated using modeling approaches or empirical methods, including experiments and observational studies.

Decision-Making Contexts

Decisions under uncertainty occur in various scenarios, such as investment choices, health diagnoses, and strategic planning.
Factors considered include potential outcomes, their associated probabilities, and how those outcomes affect overall goals.

Relative Frequency in Probability

Refers to the ratio of the number of times an event occurs to the total number of trials or observations.
Serves as an empirical way to estimate probabilities, based on actual data collected from experiments.

What Probability Quantifies

Represents the degree of belief or confidence in the occurrence of an event.
Enables individuals and organizations to navigate risks by estimating how often certain outcomes are expected to occur within a certain context.

Description

This quiz covers the fundamental concepts and applications of probability in the context of machine learning. Topics include the definition of probability, decision making under uncertainty, and practical examples of probability in everyday scenarios.