Probability Theory: Prediction Decomposition

Related lectures (29)

Page 3 of 3

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Covers conditional probability distributions and introduces the concept of conditional expected value.

Covers the theory and practical applications of linear regression.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Covers conditional density, independence of random variables, expectation, and variance calculation.

Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.

Covers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.

Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.