GEO5017 - Machine Learning for the Built Environment

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(materials will be available after each lecture. Course calendar)

Lectures (grouped by topics)	Materials
Introduction to machine learning	slides	notes (introduction)
Linear regression & gradient descent	slides	notes (linear regression) code (gradient descent)
Clustering & nearest neighbor classification	slides	notes (clustering) notes (nearest neighbor classification)
Bayesian classification & logistic regression	slides	notes (Bayesian classification) notes (logistic regression)
Support vector machine	slides	notes (SVM) notes (LagrangianDual) slides (lab) code (iris classification) code (decision boundary visualization) code (A2 starter code)
Decision trees & random forest	slides	notes (decision trees & random forest) notes (data-features-evaluation) slides (lab) code (underfitting and overfitting)
Neural networks	slides	notes (neural networks) notes (backpropagation) code (neural networks)
Convolutional neural networks	slides	notes (CNN) slides (lab) code (PyTorch and deep learning)
Guest lecture Feedback on A2 Info final exam Q&A	exam	example questions