High-dimensional statistics

Related lectures (30)

Page 2 of 3

Covers linear classification using logistic regression, regularization, and multiclass classification.

Introduces proximal operators and conditional gradient methods for composite convex minimization problems in data optimization.

Introduces k-Nearest Neighbors method and feature expansion for nonlinear machine learning through polynomial transformations.

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Covers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.

Explores nearest neighbor classifiers, bias-variance tradeoff, curse of dimensionality, and generalization bounds in supervised machine learning.