Lectures related to Quantile regression

Quantile Regression: Linear Optimization

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Regression Methods: Model Building and Inference

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

Regression Methods: Model Building and Inference

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.

Regression Analysis: Disentangling Data

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Quantile Regression: Electricity Price Prediction

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

Nonlinear Regression: Forecasting Trends

Explores machine learning techniques for nonlinear regression and forecasting trends in complex data sets.

Data-Driven Modeling: Regression

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

Analysis of Geographic Information: GeoDa

Covers the main functions of the GeoDa software for exploratory analysis.

Regression Methods: Spline Smoothing

Covers regression methods focusing on spline smoothing and penalised fitting to balance data fidelity and smoothness.

Exam Preparation: Tips and Guidelines

Offers essential guidance for exam preparation, including format details, stress management tips, and specific question explanations.

Model Selection: ROC Curves, Regression Evaluation, Conclusion

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

Regression Models: Performance and Evaluation

Explores regression model performance, learning errors, and building regression trees using the CART algorithm.

ARCH and GARCH Models: Volatility Forecasting

Covers ARCH and GARCH models for volatility forecasting in risk-factor changes.

Genomic Data Analysis: Quality Control Tools

Explores quality control tools in genomic data analysis, emphasizing robust procedures in the presence of outliers and image artifacts.

Nonparametric Regression: Kernel-Based Estimation

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Regression & Systemed Lineaires

Covers the principles of regression and linear systems, focusing on iterative methods.

Linear Regression Analysis

Introduces linear regression analysis, covering model building, predictors, coefficients, and outcome interpretation.

Scalable Synchronization Mechanisms for Manycore Operating Systems

Explores scalable synchronization mechanisms for many-core operating systems, focusing on the challenges of handling data growth and regressions in OS.

Linear and Weighted Regression: Solution to Exercises

Covers exercises on linear and weighted regression solutions, SVR problems, and Regular Least Squares.