Supervised Learning Essentials

Related lectures (31)

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

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

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

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

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

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.