GIS and Participative Planning: Case Study

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Overfitting, Cross-validation & Regularization

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Case Study: GIS and Participatory Planning

Explores GIS application in participatory planning through case studies in Thies and Seychelles, emphasizing the importance of simplicity and user engagement.

Simple validation, cross-validation, leave-one-out

Introduces techniques for obtaining unbiased risk estimates of learned predictors and their application for hyperparameter tuning.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Interpolation: Spatial Phenomena and Methods

Covers global and local deterministic interpolation methods in geographic information systems, discussing expert knowledge, method selection, and uncertainty estimation.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Metrics for Classification

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.