Lectures related to Graphical Models: Chain Rule and Variable Elimination

Probabilistic Linear Regression

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

Probability Distributions: Discrete and Continuous

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

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Probability and Measure: Fundamentals and Applications

Covers fundamental concepts of probability theory and measure theory, including joint probabilities, random variables, and the central limit theorem.

Probability Theory: Midterm Solutions

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

Discrete Random Variables: Medical Testing

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

Linear Regression: Theory and Applications

Covers the theory and practical applications of linear regression.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Conditional Probability Distributions

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

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Expectation Maximization: Learning Parameters

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

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Probability Fundamentals

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

Probability Theory: Prediction Decomposition

Explains prediction decomposition in probability theory with examples and calculations.

Random Vectors and Stochastic Models for Communications

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

Statistics for Data Science: Basics and Modelling

Introduces statistical modelling basics, probability theory, and key concepts for data science applications.

Transformations of Joint Densities

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

Random Vectors: Stochastic Models for Communications

Covers random vectors, joint probability, and Gaussian random variables in communication models.

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.