Lecture

Gradient Descent: Linear Regression

Related lectures (122)
Linear and Logistic Regression
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Linear Regression: Basics and Applications
Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.
Linear and Logistic Regression
Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.
Linear Regression
Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Linear Models for Classification
Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.
Linear Regression: Basics and Applications
Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.
Linear and Weighted Regression: Optimal Parameters and Local Solutions
Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.
Overfitting, Cross-validation, Regularization
Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.
Gradient Descent and Linear Regression
Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.
Linear Models: Recap and Logistic Regression
Covers linear models, binary classification, logistic regression, and model evaluation metrics.

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.