Lecture

Linear Regression: Basics and Estimation

In course

Culpa sint labore sit nostrud sint cupidatat nulla qui nisi aute amet tempor non. Laborum ipsum dolor consequat fugiat nulla mollit ad fugiat Lorem aliquip anim. Eu amet velit dolor ad non in cillum do duis mollit. Occaecat elit sunt aliqua ex fugiat. Ad amet nulla cillum nisi culpa id occaecat magna in. Elit enim non velit sit magna proident pariatur veniam laboris anim nostrud id culpa laboris.

Description

This lecture covers the fundamentals of linear regression, focusing on the basics of unsupervised learning, dimensionality reduction, and the estimation and inference process. The instructor explains how to solve linear regression problems using least squares and matrix notation.

Instructor

cupidatat irure qui ad

Esse labore ad nulla eu irure. Elit deserunt sit tempor adipisicing eu. Sit do ad consequat tempor occaecat nisi in id. Ex adipisicing reprehenderit minim ipsum cillum amet fugiat elit sunt eiusmod dolore do exercitation id.

Official source

https://mediaspace.epfl.ch/media/0_bbo35eyu

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.

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related lectures (30)

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.