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Related lectures (32)

Regression Methods: Model Building and Inference

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

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation

Explores maximum likelihood estimation, logistic regression, covariance estimation, and support vector machines for classification problems.

Inference: Model Checking

Covers iterative weighted least squares, generalized linear models, and model checking.

General Linear Model: Model Selection

Explores the General Linear Model, significance testing, model selection, and parameter inference.

PCA: Directions of Largest Variance

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Linear Regression Basics

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

Kalman Filter: Linearized Models

Explains linearized and non-linear Kalman Filter models and the use of Jacobians.

CMOS Circuits: Metabolites Detection

Explores CMOS circuits for metabolites detection in fixed-voltage cells, covering operational amplifier features, saturation risks, temperature compensation, and current measurement techniques.

Nonparametric Regression

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Mixture models: latent classes

Explores latent classes in mixture models for discrete choice modeling using the Swissmetro dataset, discussing class-specific models and random parameters estimation.

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