Concept

Logistic distribution

Related lectures (31)

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.

Logistic Regression

Covers logistic regression for linear classification and unsupervised dimensionality reduction techniques.

Linear Regression and Logistic Regression

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

Logistic Regression: Classification

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Model Specification: The Error Term

Delves into the binary choice model, error term specification, and Extreme Value distribution properties.

Linear Models for Classification: Multi-Class Extensions

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

Logistic Regression: Fundamentals and Applications

Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.

Linear and Logistic Regression

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Sparsest Cut: Bourgain's Theorem

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Linear Models for Classification

Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Parametric Models: Logistic and Dirichlet Densities

Explores logistic and Dirichlet densities, parametric models, and asymmetric alternatives.

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Linear Models: Recap and Logistic Regression

Covers linear models, binary classification, logistic regression, and model evaluation metrics.

Markov chains

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Generalized Linear Regression

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Logistic Regression: Probability Mapping

Explains logistic regression, mapping predictions to probabilities and optimizing likelihood.

Asymmetric Parametric Models: Extremal Coefficient

Explores asymmetric parametric models and the extremal coefficient in extreme value dependence.

Dynamic Systems: Course Review

Covers dynamic systems, trajectories, growth models, stability of fixed points, and linearization of models.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.