Lecture

Linear and Logistic Regression

In course
DEMO: ad et dolor
Sunt ex exercitation officia ut do voluptate ut. Ea minim exercitation veniam amet irure officia in eiusmod. Consectetur cupidatat non ex nisi sit adipisicing eiusmod.
Login to see this section
Description

This lecture introduces the concepts of linear and logistic regression. It covers the parametric approach, underfitting, overfitting, performance metrics, normal equation, gradient descent, multiple features, and the activation function. The instructor explains how to predict concrete strength and classify buildings based on size, emphasizing the importance of minimizing loss functions and understanding the trade-offs between model complexity, bias, and variance.

Instructors (2)
velit aliquip adipisicing aute
Amet eiusmod aliqua eiusmod non cillum aute incididunt excepteur. Aliquip voluptate sit ullamco do eiusmod voluptate laboris. Ad quis commodo consequat et ipsum aute culpa incididunt veniam esse. Nisi qui fugiat laborum nostrud aute reprehenderit fugiat consectetur dolore. Ea minim quis magna aute elit officia voluptate cillum.
ea amet consectetur elit
Dolore occaecat aute laboris ad nostrud in esse consequat ea duis. Laboris excepteur ex aliquip sunt ea. Irure ut eiusmod laborum adipisicing deserunt cillum sunt deserunt. Magna ad tempor elit pariatur consequat ad est deserunt sunt ea eu. Pariatur veniam ea tempor Lorem aliqua. Tempor sunt minim id anim eu ut reprehenderit commodo voluptate est minim qui.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (39)
Polynomial Regression: Overview
Covers polynomial regression, flexibility impact, and underfitting vs overfitting.
Error Decomposition and Regression Methods
Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.
Generalized Linear Regression: Classification
Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.
Flexibility of Models & Bias-Variance Trade-Off
Delves into the trade-off between model flexibility and bias-variance in error decomposition, polynomial regression, KNN, and the curse of dimensionality.
Linear Regression and Logistic Regression
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Show more