Survival Data Analysis

Related lectures (31)

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Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

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

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

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.