Generative Learning & Model Properties

Related lectures (31)

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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 generative learning algorithms, decision rules, and Gaussian distribution properties in machine learning.

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

Covers Gaussian discriminant analysis, log-likelihood, supervised learning, and logistic regression.

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

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

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

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