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

The assessment of this course consists of two group assignments and the final exam. The final grade is based on the evaluation of both the assignments and the final exam, i.e.,

- Group assignments (40%). All assignments have equal weight in the final grade. It is possible to resubmit your work after incorporating the feedback/suggestions received from the teachers. However, the evaluation of an assignment is mainly based on the first submission. Students who have improved their work may receive a slightly higher grade depending on the significance of the improvement (but no more than 5%). Assignments submitted after the deadline: the grade will be deducted with 10% per day late, and not accepted after 3 days late.

- Final exam (60%). The final exam consists of multiple-choice questions and open questions. Example questions will be given two weeks before the exam.

To pass the course, the following applies:

1. a total average of 5.75 or above is required to pass the course, and

2. all assignments and the final exam must be graded greater or equal to 5.5.

Repair and retake options: any component of the assessment graded 5.75 or lower is eligible for one more opportunity to retake (for the exam) or repair (for assignments). A repair is a revision or addition to an existing assignment, and a successful repair will only be assessed with a 6.0. A retake is an entirely new examination.