Teaching

Machine Learning for the Built Environment Permalink

Graduate course, Delft University of Technology, 2021

Liangliang Nan, Shenglan Du, Nail Ibrahimli
2021-2022 Q3, 2022-2023 Q3

This course is introductory for machine learning to equip students with the basic knowledge and skills for further study and research in machine learning. It introduces the theory/methods of well-established machine learning and a few state-of-the-art deep learning techniques for processing geospatial data (e.g., point clouds). 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.

Photogrammetry and 3D Computer Vision Permalink

Graduate course, Delft University of Technology, 2020

Liangliang Nan, Nail Ibrahimli
2020-2021 Q3, 2021-2022 Q4, 2022-2023 Q4

Photogrammetry and 3D Computer Vision (i.e., 3DV) aim at recovering the structure of real-world objects/scenes from images. This course is about the theories, methodologies, and techniques of 3D computer vision for the built environment. In the term of this course, you will learn the basic knowledge and algorithms in 3D computer vision through a series of lectures, reading materials, lab exercises, and group assignments.