Lectures related to Least-angle regression

Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Generalization Error in Learning with Random Features

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

Regression: Interactive Lecture

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

ANOVA: Partitioning Total SS

Covers ANOVA method, focusing on partitioning total sum of squares into treatment and error components, mean square calculations, Fisher statistic, and F-distribution.

Confidence Bounds: Key Concepts and Variance Analysis

Covers key assumptions, variance analysis, and confidence intervals in linear regression.

Model Selection Methods in Biostatistics

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

LASSO Regression: Sparse Signal Induction

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

Linear and Weighted Regression: Optimal Parameters and Local Solutions

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Quantile Regression: Electricity Price Prediction

Explores quantile regression for electricity price prediction using time series data, regularization, and the kernel trick.

Causal Inference: Understanding Treatment Effects

Explores treatment effects, confounding variables, and challenges in interpreting observational study results.

Machine Learning Phases

Explains the importance of training, validation, and test sets in machine learning phases.

Geometry of the Lasso

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Why Standard ML is Not Sufficient: Learning and Adaptive Control

Delves into the challenges of using standard ML for stable robot control.

Linear Regression: Confidence Bounds

Covers linear regression coefficients, confidence intervals, and R's calculation methods.

Singular Value Decomposition: Ridge and LASSO

Covers the concept of Singular Value Decomposition and its application in regression techniques.

Comparing L1 and L0 + Greedy algorithms

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.