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Concept

Ensemble learning

Related lectures (30)

Building Robust Ensembles via Margin Boosting

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

Crash course on ensemble methods: Bagging and Boosting

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

Decision Trees and CLT's: Inference and Machine Learning

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

Adaboost: Boosting Methods

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

Advanced Machine Learning: Bagging

Explores ensemble learning methods like Bagging and Boosting to improve model performance through aggregation and the selection of unstable models.

Bagging and Random Forests

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

Supervised Learning: k-NN and Decision Trees

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

Structured Classifications: Decision Trees and Boosting

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

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.

Boosting: Adaboost Algorithm

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

Advanced Machine Learning: Boosting

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

Addressing Overfitting in Decision Trees

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

Statistical Physics of Learning

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Threat Modeling: Adversarial Examples and Backdoors

Covers threat modeling in deep learning for autonomous vehicles, focusing on defending against adversarial examples and backdoors.

Randomized Classifiers in the ROC Plane

Explores how randomized classifiers achieve convex combinations in the ROC plane.

Entity & Information Extraction

Explores information extraction using classifiers, features, and syntactic analysis.

Learning in the Presence of Distribution Shifts: How does the Geometry of Perturbations Play a Role?

Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.

Decision Trees: Induction & Attributes

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

Decision Trees: Overfitting and Randomization

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

Virtual World: Engineering Reality MOOC: Space Mission Design and Operations

Explores a unique world where you can engineer reality and create innovative tools.