Decision Trees: Regression and Classification

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Page 3 of 4

Receiver-Operator Characteristics: ROC Curves

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

Optimization in Machine Learning: Gradient Descent

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

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Kernel Regression: K-nearest Neighbors

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

Model Selection Criteria: AIC, BIC, Cp

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.

Error Decomposition and Regression Methods

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

Kernel Methods and Regression

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

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.