Lecture

Linear Regression: Introduction and Training

Related lectures (89)
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.
Machine Learning Basics
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Introduction to Machine Learning
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Machine Learning Fundamentals
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Data Representation: PCA
Covers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

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.