Lectures related to Distribution Estimation

Covers t-tests, p-values calculation, and comparison of coefficients.

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.

Probability and Statistics

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

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

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.