1.5. Giá trị kỳ vọng và phương sai. Xác suất & thống kê trong học máy

playlist: https://www.youtube.com/playlist?list=PLIpLw6v7Z1qmwduFFgLMfat93-FUMjpCR

Probability & Statistics Essentials for Machine Learning

Expected Value and Variance

https://apxml.com/courses/probability-statistics-essentials-ml/chapter-1-probability-foundations-revisited/expected-value-variance

Random variables represent numerical values assigned to outcomes from a sample space. To understand and analyze these variables, it is important to summarize their characteristics beyond simply listing possible values. Describing the 'center' and 'spread' of the distribution of these values provides a more complete picture. Expected value and variance are the main measures that provide this summary.
Expected Value: The Center of Mass

The expected value, denoted as E[X]E[X] or sometimes μXμX​ (or simply μμ), represents the weighted average of the possible values a random variable XX can take, where the weights are the probabilities of those values. Intuitively, if you were to repeat an experiment involving XX many times and calculate the average of the outcomes, that average would converge to the expected value E[X]E[X]. It's like the "center of mass" of the probability distribution.

Видео 1.5. Giá trị kỳ vọng và phương sai. Xác suất & thống kê trong học máy канала Le Hoang Long Long