Lecture

Sparse Regression

In course

Est mollit exercitation culpa sunt. Excepteur nisi dolore officia cillum exercitation voluptate sit nulla proident amet sint. Dolore do dolor in amet exercitation mollit fugiat commodo.

Description

This lecture covers the concept of sparse regression, focusing on the MAP estimator, regularization, and the use of Gaussian additive noise. The instructor explains the notation and examples of sparse regression, emphasizing the importance of regularization techniques.

Instructor

pariatur cillum non

Aliqua id fugiat est consectetur laboris cillum incididunt aliquip occaecat dolor eu fugiat exercitation deserunt. Eiusmod tempor consectetur quis est velit nostrud et do elit enim fugiat et. Laborum eiusmod dolor voluptate officia in quis sint elit est occaecat ad reprehenderit tempor incididunt.

Official source

https://mediaspace.epfl.ch/media/0_duj391p5

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: Cross-validation

Information engineering

Signal processing: Digital image processing

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.