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Related lectures (30)

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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.

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

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

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

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