Machine Learning for the Built Environment

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. The topics of the course include:

- Introduction to machine learning

[-] Applications of machine learning

[-] The scope of machine learning

-) Regression vs classification

-) Supervised learning vs unsupervised learning

[-] Limits and dangers of machine learning

- Clustering

[-] K-means

[-] Hierarchical

[-] Density-based

- Linear regression

[-] Closed-from solution

[-] Solution via optimization

[-] Gradient descent

- Classification

[-] K-nearest neighbors

[-] Bayesian classification

[-] Logistic regression

[-] Support vector machine (SVM)

-) Maximum margin classification

-) Soft-margin SVM

[-] Decision trees and random forest

- Neural networks

[-] Multi-layer perception

[-] Backpropogation

- Deep learning (focusing on CNN)

[-] Convolution

[-] CNN architecture