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Classification

Associated Notebooks:

Classification Basics - Intro to classification problems, accuracy metrics, multi-class classification and loss functions.
Generalized Linear Models - Perceptron, logistic regression, margin cost, and support vector machine model derivation and implementation.
Alternate Classification Models - Demonstrations of k-Nearest Neighbors, Naive Bayes, and decision trees.
High-dimensional Classification - Implementation of high-dimensional classification using the perovskite dataset.