Logistic Regression: Statistical Inference and Machine Learning

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Linear Binary Classification

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Statistical Justification of Least Squares

Explores the statistical justification of Least Squares and Generalized Linear Models.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Logistic Regression: Probability Modeling

Covers logistic regression for binary classification using probability modeling and optimization methods.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.