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Related lectures (32)

Modern Regression: Inference and Models Log in to Mediaspace to watch this video

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

Regression Methods: Model Building and Inference Log in to Mediaspace to watch this video

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

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Modern Regression: Overdispersion and Model Assessment Log in to Mediaspace to watch this video

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

Regression Methods: Model Building and Inference Log in to Mediaspace to watch this video

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

Optimization in Statistics and Machine Learning: Maximum Likelihood Estimation Log in to Mediaspace to watch this video

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

Inference: Model Checking Log in to Mediaspace to watch this video

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

General Linear Model: Model Selection Log in to Mediaspace to watch this video

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

PCA: Directions of Largest Variance Log in to Mediaspace to watch this video

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

Linear Regression Basics Log in to Mediaspace to watch this video

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 Log in to Mediaspace to watch this video

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

Regularization in Machine Learning Log in to Mediaspace to watch this video

Introduces regularization techniques to prevent overfitting in machine learning models.

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