Decision Trees: Regression and Classification

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Related lectures (32)

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Explains ROC curves, Precision-Recall curve, RMSLE, and model validation.

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Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Probabilistic Models for Linear Regression

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Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

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Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Validation and k-Nearest Neighbors Method

Introduces supervised learning concepts and the k-Nearest Neighbors method for classification and regression tasks.

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Kernel Methods and Regression

Covers kernel methods, kernel regression, RBF kernel, and SVM for classification.

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Covers feature selection, kernel regression, and neural networks through exercises.

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