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
Eigenvalues and EM Algorithm
Graph Chatbot
Related lectures (29)
Previous
Page 2 of 3
Next
Probability and Statistics: Independence and Conditional Probability
Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.
Supervised Learning: Likelihood Maximization
Covers supervised learning through likelihood maximization to find optimal parameters.
Elements of Statistics: Probability and Random Variables
Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.
Markov Chains: Theory and Applications
Covers the theory and applications of Markov chains, focusing on key concepts and properties.
Conditional Density and Expectation
Explores conditional density, expectations, and independence of random variables with practical examples.
Stationary Distribution in Markov Chains
Explores the concept of stationary distribution in Markov chains, discussing its properties and implications, as well as the conditions for positive-recurrence.
Quantifying Statistical Dependence
Delves into quantifying statistical dependence through covariance, correlation, and mutual information.
Supervised Learning Essentials
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Linear Regression: Theory and Applications
Covers the theory and practical applications of linear regression.
Maximum Likelihood Decision
Explains decision by maximum likelihood and the use of logarithms.