• Mar. 29. A few example questions of the final exam can be found here. The final exam will consist similar (but different) questions.

  • Mar. 28. Our last course meeting will be on Mar. 29 (Tuesday), 13:45 – 15:45, in room BK-IZ U. In this meeting, we will have a quick review of the entire course and show a few example questions for the final exam, followed by Q&A.

  • All news ...


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.


The topics of this course cover the fundamental of machine learning and deep learning, including:


After finishing this course, the students will have gained the theory of commonly used machine learning techniques and the skills to apply them for processing geospatial data. Specifically, the students will be able to:


The assessment of this course consists of two (or three) group assignments and the final exam. The final grade will be based on the contribution in doing the assignments and the final exam, breaking down to:

Both assignments and the final exam have to meet the minimum requirement (i.e., 5.5) to pass the course. A total of 6.0 or above is necessary to pass the course.


Course logistics


Liangliang Shenglan Nail
Liangliang Nan Shenglan Du Nail Ibrahimli
LiangliangNan#0976 Shenglan Du#2136 nibrahimli#5857

Copyright @Liangliang Nan. 2022