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Related lectures (30)

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Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.

Linear Models and Overfitting

Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.

Non-Negative Definite Matrices and Covariance Matrices

Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.

Experimental Design in Genomic Data Analysis

Emphasizes experimental design in genomic data analysis, addressing technical variability, batch effects, and statistical solutions.

Regularization Techniques

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Optimality and Asymptotics

Explores the optimality of the Least Squares Estimator and its large sample distribution.

MLE Applications: Binary Choice Models

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.

Neural Tangent Kernel: Generalization in Deep Learning

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

Linear and Quadratic Models of Scheffé

Explores linear and quadratic models of Scheffé in mixing plans and ternary schemes, emphasizing constraints and representations.