Lecture

Data Representations and Processing

In course
DEMO: ut ad nostrud
Ipsum cillum reprehenderit aliqua anim adipisicing sint duis excepteur laboris non esse ex sunt voluptate. Incididunt dolor consequat consequat enim anim dolore laborum nisi pariatur sit culpa irure consequat anim. Sunt laboris commodo in cupidatat nisi nulla velit.
Login to see this section
Description

This lecture covers the importance of data representations in machine learning, focusing on techniques like Bag of Words for text and visual dictionaries for images. It also discusses the challenges of imbalanced data and strategies for data normalization, cleaning, and preprocessing.

Instructor
quis exercitation commodo
Minim nulla magna ullamco ad incididunt aliqua magna non. Sint enim non qui ipsum. Cupidatat officia labore officia sint. Eu non laborum cillum eiusmod veniam amet ut non nostrud qui ex sint.
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 (34)
Vision-Language-Action Models: Training and Applications
Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.
Document Analysis: Topic Modeling
Explores document analysis, topic modeling, and generative models for data generation in machine learning.
Data Representation: PCA
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Show more

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.