Lectures related to High-dimensional statistics

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

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Explores generalization error in learning theory, emphasizing structured data and the replica method.

Explores Generalised Additive Models and methods to tackle dimensionality issues efficiently.

Introduces k-Nearest Neighbors for classification and feature expansion to handle nonlinear data through transformed inputs.

Explores over-learning, generalization, and under-learning in machine learning models.

Explores Generalized Linear Models, Bayesian methods, compressed sensing, and perception in high-dimensional statistics.

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Explores LASSO regression for inducing sparsity in signals through gradient descent.

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.