Skip to main content
Graph
Search
fr
|
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Expectation Maximization: Learning Parameters
Graph Chatbot
Related lectures (29)
Previous
Page 1 of 3
Next
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
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 and Statistics: Fundamentals
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Probability and Statistics
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Conditional Probability: Prediction Decomposition
Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.
Probability and Statistics
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Probability Theory: Midterm Solutions
Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.
Probability Basics: Theory and Applications
Covers basic probability theory, conditional probability, independence, and Bayes' Theorem.
Introduction to Inference
Covers the basics of probability theory, random variables, joint probability, and inference.
Statistical Theory: Fundamentals
Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.
