Lectures related to Modern Regression: Applications and Examples

Linear Models: Part 1

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Linear Regression: Statistical Inference and Regularization

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

Probabilistic Models for Linear Regression

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

Regression Methods: Model Building and Inference

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

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Linear Regression Basics

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Nonlinear Machine Learning: k-Nearest Neighbors and Feature Expansion

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Linear and Weighted Regression: Optimal Parameters and Local Solutions

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

Parametric Models

Explores statistical estimation, regression models, and model selection in parametric models.

Linear Regression Basics

Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Linear Regression: Beyond the Basics

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Model Checking and Residuals

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Modern Regression: Overdispersion and Model Assessment

Explores overdispersion, model assessment, and regression techniques for count data.

Kernel Regression: K-nearest Neighbors

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Linear Regression: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Venice Sea Levels Analysis

Analyzes Venice sea levels from 1887 to 2017, focusing on maximum levels and trend estimation.

Data-Driven Modeling: Regression

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.