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Lecture
Probability Theory: Basics and Applications
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Related lectures (30)
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Calculations of Expectation
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
Conditional Probability: Prediction Decomposition
Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.
Linear Combinations: Moment-Generating Functions
Explores moment-generating functions, linear combinations, and normality of random variables.
Statistics: Random Variables and Probability Density Functions
Introduces random variables, probability density functions, and Gaussian distribution in statistics.
Introduction to Inference
Covers the basics of probability theory, random variables, joint probability, and inference.
Convergence in Probability
Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Law of Large Numbers: Strong Convergence
Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
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 and Random Variables: Key Concepts Explained
Explains key concepts in probability, including conditional probability, independence, and random variables, with practical examples to illustrate their applications.
