Linear Discriminant Analysis: Generative Methods

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Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Explores unsupervised learning techniques for reducing dimensions in data, emphasizing PCA, LDA, and Kernel PCA.

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers supervised learning, classification using logistic regression, and challenges in optimization.