This is an introductory course for machine learning to equip students with the basic knowledge and skills for further study and research of machine learning. It introduces the theory/methods of well-established machine learning and state-of-the-art deep learning techniques for processing geospatial data (e.g., point clouds). The students will also gain hands-on experiences by applying commonly used machine learning techniques to solve practical problems through a series of lab exercises and assignments.
After the course, the students will be able to:
- explain the impact, limits, and dangers of machine learning; give use cases of machine learning for the built environment;
- explain the main concepts in machine learning (e.g., regression, classification, unsupervised learning, supervised learning, overfitting, training, validation, cross-validation, learning curve);
- explain the principles of well-established unsupervised and supervised machine learning techniques (e.g., clustering, linear regression, Bayesian classification, logistic regression, SVM, decision tree, random forest, and neural networks);
- preprocess data (e.g., labelling, feature design, feature selection, train-test split) for applying machine learning techniques;
- select and apply the appropriate machine learning method for a specific geospatial data processing task (e.g., object classification);
- evaluate the performance of machine learning models.
