Week 6 Lecture 1 | Decision Trees - Introduction

Week 12 Lecture 1 | Learning TheoryПодробнее

Week 11 Lecture 1 | Gaussian Mixture ModelsПодробнее

Week 10 Lecture 1| Partitional ClusteringПодробнее

Week 9 Lecture 1 | Undirected Graphical Models - Introduction and FactorizationПодробнее

Week 8 Lecture 1 | Gradient BoostingПодробнее

Week 7 Lecture 1 | Evaluation and Evaluation Measures IПодробнее

Week 6 Lecture 9 | Decision Trees - ExampleПодробнее

Week 6 Lecture 8 | Decision Trees - Instability, Smoothness, Repeated SubtreesПодробнее

Week 6 Lecture 7 | Decision Trees - Missing Values, Imputation, Surrogate SplitsПодробнее

Week 6 Lecture 6 | Decision Trees - Multiway SplitsПодробнее

Week 6 Lecture 5 | Decision Trees - Categorical AttributesПодробнее

Week 6 Lecture 4 | Decision Trees for Classification - Loss FunctionsПодробнее

Week 6 Lecture 3 | Decision Trees - Stopping Criteria and PruningПодробнее

Week 6 Lecture 2 | Regression TreesПодробнее

Week 5 Lecture 1 | Artificial Neural Networks I - Early ModelsПодробнее

Week 4 Lecture 6 | Hingle Loss formulation of SVM ObjectiveПодробнее

Week 4 Lecture 1 | Separating Hyperplane Approaches - Perceptron LearningПодробнее

Week 3 Lecture 6 | Weka TutorialПодробнее

Week 3 Lecture 1 | Linear ClassificationПодробнее