Lecture

Linear Regression: Estimation and Prediction

Related lectures (51)
Linear Regression
Covers linear regression for estimating train speed using least squares and regularization.
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Linear Regression: Basics and Estimation
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Linear Regression and Gradient Descent
Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.
Linear Regression: Statistical Inference and Regularization
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Generative Models: Self-Attention and Transformers
Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Weighted Least Squares Estimation: IRLS Algorithm
Explores the IRLS algorithm for weighted least squares estimation in GLM.
Linear Regression: Regularization
Covers linear regression, regularization, and probabilistic models in generating labels.
Linear MM SE Estimation
Covers the principles of linear MM SE estimation and the minimization of errors in linear regression.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.