Linear and Quadratic Models of Scheffé

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Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

Statistical Inference: Linear Models

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Linear Models for Classification: Multi-Class Extensions

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

Neural Tangent Kernel: Generalization in Deep Learning

Explores the neural tangent kernel in deep learning, analyzing generalization and network behavior.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Linear Models for Classification

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

Causality for Robust ML

Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.

Robust Regression: Methods and Applications

Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.

Linear Models: Classification

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

General Linear Model: Symmetric Matrices

Covers the general linear model and the concept of symmetric matrices.