Concept

Conditional probability distribution

Related lectures (27)

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Conditional Density and Expectation

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

Transformations of Joint Densities

Covers the transformations of joint continuous densities and their implications on probability distributions.

Probability Distributions: Discrete and Continuous

Covers discrete and continuous probability distributions, including joint and conditional probabilities.

Multivariate Statistics: Normal Distribution

Covers the multivariate normal distribution, properties, and sampling methods.

Probability Theory: Midterm Solutions

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Linear Regression: Theory and Applications

Covers the theory and practical applications of linear regression.

Multinomial Distribution

Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Discrete Random Variables: Medical Testing

Explores discrete random variables, joint probabilities, and medical testing quality using Bayes' theorem.

Basic Properties of Conditional Expectation

Covers basic properties of conditional expectation and Jensen's inequality in probability theory.

Random Vectors and Stochastic Models for Communications

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Stochastic Calculus: Lecture 1

Covers the essentials of probability, algebras, and conditional probability, including the Borel o-algebra and Poisson processes.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Introduction to Probability Theory

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

Conditional Probability

Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.

Expectation Maximization: Learning Parameters

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Conditional Density and Expectation

Explores conditional density, expectations, and independence of random variables with practical examples.

Conditional Expectation: Basics

Introduces the basics of conditional expectation, covering definitions, properties, and examples in the context of random variables.