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Decision tree learning

Related lectures (32)

Structured Classifications: Decision Trees and Boosting

Explores decision trees, overfitting elimination, boosting techniques, and their practical applications in predictive modeling.

Decision Trees: Induction & Attributes

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Boosting: Adaboost Algorithm

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

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Advanced Machine Learning: Boosting

Covers weak learners in boosting, AdaBoost algorithm, drawbacks, simple weak learners, boosting variants, and Viola-Jones Haar-Like wavelets.

Decision Forests: Structure and Training

Covers decision forests, training, weak learners, entropy, boosting, 3D pose estimation, and practical applications.

Decision Trees and CLT's: Inference and Machine Learning

Explores decision trees, ensembles, CLT, inference, machine learning, diagnostic methods, boosting, and variance estimation.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Machine Learning Basics: Supervised and Unsupervised Learning

Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.

Adaboost: Boosting Methods

Explains Adaboost algorithm for building strong classifiers from weak ones, with a focus on boosting methods and face detection.

Addressing Overfitting in Decision Trees

Explores overfitting in decision trees and introduces random forests as a solution.

Crash course on ensemble methods: Bagging and Boosting

Covers ensemble methods, AdaBoost, and setting weights for classifiers.

Building Robust Ensembles via Margin Boosting

Delves into building robust ensembles through margin boosting for improved adversarial defense in machine learning models.

Decision Trees: Overfitting and Randomization

Explores decision trees, overfitting, and randomization in supervised learning, emphasizing the importance of managing variance and feature selection.

Bagging and Random Forests

Covers ensembling, bagging, random forests, variable importance, and OOB cross-validation in machine learning.

Kinetic Isotope Effects and Linear Free Energy Relationships

Explores kinetic isotope effects and Linear Free Energy Relationships, introducing machine learning methods for chemistry applications.

Decision Trees and Boosting Log in to Mediaspace to watch this video

Explores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.

Regression Trees and Ensemble Methods in Machine Learning Log in to Mediaspace to watch this video

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

AdaBoost: Decision Stumps Log in to Mediaspace to watch this video

Explores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.

Ensemble Methods: Random Forest Log in to Mediaspace to watch this video

Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.