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Related lectures (5)
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RANSAC: Robust Outlier Detection and Applications
Explores the RANSAC algorithm for outlier detection and its diverse applications in Machine Learning.
Advanced Machine Learning: Bagging
Explores ensemble learning methods like Bagging and Boosting to improve model performance through aggregation and the selection of unstable models.
Ensemble Methods: Random Forest
Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Ensemble Methods: Random Forests
Covers ensemble methods like random forests and Gaussian Naive Bayes, explaining how they improve prediction accuracy and estimate conditional Gaussian distributions.
Crash course on ensemble methods: Bagging and Boosting
Covers ensemble methods, AdaBoost, and setting weights for classifiers.
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