Decision Trees and Boosting

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Related lectures (33)

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.

Nonlinear Supervised Learning

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

Decision Trees and CLT's: Inference and Machine Learning

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

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.

Boosting: Adaboost Algorithm

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

Supervised Learning: k-NN and Decision Trees

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

Decision Forests: Structure and Training

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

Kinetic Isotope Effects and Linear Free Energy Relationships

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

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Addressing Overfitting in Decision Trees

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

Interpretable Machine Learning: Sparse Decision Trees and Interpretable Neural Networks

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Advanced Machine Learning: Boosting

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

Classification: Decision Trees and kNN

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.

Decision Trees and Random Forests: Concepts and Applications Log in to Mediaspace to watch this video

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

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.

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

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

Decision Trees: Classification Log in to Mediaspace to watch this video

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Recurrent Neural Networks: Language Detection Log in to Mediaspace to watch this video

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Introduction to Data Science Log in to Mediaspace to watch this video

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

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