Gaussian Discriminant Analysis

Related lectures (29)

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

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

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

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

Covers the multivariate normal distribution, properties, and sampling methods.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers supervised learning through likelihood maximization to find optimal parameters.

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

Explores generative learning, class priors, and maximum likelihood estimation in classification models.

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