Applied Machine Learning: Features and Models

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.

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores machine learning algorithms, feature selection techniques like FAST and BRIEF descriptors, and the limitations of deep learning.

Explores machine learning in brain imaging, focusing on spatial patterns, emotions, and classifier trade-offs.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Explores the RANSAC algorithm for outlier detection and its diverse applications in Machine Learning.

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.