This course is introductory to 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 fundamentals 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 group assignments and the final exam. The final grade is based on the evaluation of both the assignments and the final exam, breaking down into:

Pass or fail? Grading is done on a 10-point scale, and the final marks are rounded off to the nearest full and half figures. To pass the course, all assignments and the final exam must be graded greater or equal to 5.5, and a total of 5.75 or above is required to pass the course.

Repair and retake options. Any component of the assessment graded 5.5 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.


To embark on this exciting journey through the course, it is crucial to have a solid grasp of fundamental calculus, linear algebra, and statistics. Additionally, we assume you have completed a basic Python programming course in the past. Here is a breakdown of the key prerequisites: To ensure your readiness, we have designed a set of questions for self-assessment. Please complete this assessment to determine if you are ready for the course:

Course logistics


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