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Quantile regression
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Related lectures (32)
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Machine Learning Phases
Explains the importance of training, validation, and test sets in machine learning phases.
Nonlinear Regression: Solutions to Exercises
Explores nonlinear regression using Gaussian components for density modeling.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Gaussian Process Regression: Kernels and Comparisons
Explores Gaussian Process Regression kernels, computational costs, and comparisons with Ridge Regression and other non-linear regression techniques.
Linear Regression: Beyond the Basics
Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.
Robust Regression in Genomic Data Analysis
Explores robust regression in genomic data analysis, focusing on downweighting large residuals for improved estimation accuracy and quality assessment metrics like NUSE and RLE.
Why Standard ML is Not Sufficient: Learning and Adaptive Control
Delves into the challenges of using standard ML for stable robot control.
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Linear Models: Recap and Extensions
Covers linear models, multi-class classification, k-Nearest Neighbors, and feature expansion techniques.
Linear Regression: Theory and Applications
Covers the theory and practical applications of linear regression.
Linear Models: Classification Basics
Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.
Regression Methods: Model Building and Diagnostics
Explores regression methods, covering model building, diagnostics, inference, and analysis of variance.
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