Lecture

Decision and regression trees

Graph Chat

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.

Related lectures (64)

Quantifying Performance: Misclassification and F-Measure

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Linear Models for Classification

Explores linear models for classification, logistic regression, and gradient descent in machine learning.

Probability Theory: Joint Marginals and Granger Causality

Covers joint marginals and Granger causality in probability theory, explaining their implications in predicting outcomes.

Boosting: Adaboost Algorithm

Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.

Topic Models: Latent Dirichlet Allocation

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.

Decision Trees and Boosting

Introduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.

SVM and Multiclass Classification

Covers SVM and multiclass classification using one-vs-all and one-vs-one approaches.

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Bayesian Statistics: Regularization and Divergence

Covers Kullback-Leibler divergence, regularization, and Bayesian statistics to combat overfitting in machine learning models.

Linear Models for Classification

Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Neural Networks: Multi-layers

Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Statistical Analysis: Dispersion and Normal Values

Explores statistical dispersion and its impact on determining normal values and data analysis.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Thermodynamics: Entropy, Spontaneity, and Free Energy

Explores heat capacity, entropy, spontaneity, and free energy in chemical reactions, emphasizing the relationship between entropy and disorder.

Lecture 03 (2024)

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Advanced Machine Learning: Brief review of C-SVM

Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.