Logistic regression

Formal sciences
Statistics
Data analysis
Regression analysis

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Related lectures (500)

Generative Models: Logistic Regression & Gaussian Distribution

Explores generative models, logistic regression, and Gaussian distribution for approximating posterior probabilities and optimizing model performance.

Oxygen Consumption Analysis

Delves into oxygen consumption analysis, covering regression, error interpretation, and the Michaelis-Menten model application.

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Sparse Regression

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

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Linear and Logistic Regression

Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

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