Linear Regression: Least Squares and Normal Equations

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.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores Gaussian linear regression, design matrix, least squares estimation, and geometric interpretation in linear regression analysis.

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores Singular Value Decomposition theory, linear system solutions, least squares, and data fitting concepts.

Covers linear regression, least squares method, polynomial regression, and data classification in image analysis.

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Explores linear regression with and without covariates, covering models captured by independent distributions and tools like subspaces and orthogonal projections.

Explores Bayesian inference, multiclass classification, logistic regression, and linear regression inference.

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

Explores linear models, least squares, Gaussian vectors, and model selection methods.

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

Introduces simple and multiple normal linear regression, and maximum likelihood estimation with practical examples.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.