Generalized Linear Models

Related lectures (30)

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Explores noise in electronics, covering average power, signal-to-noise ratio, deterministic and random signals, and noise amplification.

Introduces the fundamentals of noise in electronics, covering origins, signal types, power characteristics, and noise sources.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

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

Explores laser fundamentals, noise sources, and beam steering technologies using optical fibers and ultrafast lasers.

Explores environmental legislation, pollution control, noise regulations, and construction impact on the environment.

Discusses various noise sources in optical detectors, including shot noise, thermal noise, and their implications on detection performance.

Covers strategies to combat noise sources in electronics measurements and explores techniques to mitigate noise and improve measurement accuracy.

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.