Grading system
The final grade will be obtained as the combination of the following:
- 50.00% - Course Project
- 30.00% - Midterm exam
- 20.00% - Homeworks
Homeworks
Homeworks will be assigned at the end of each topic. They consist of both pen and paper questions and programming exercises. The submitted codes must be properly commented and implementation choices must be justified (this is as important as the code itself and counts towards the final mark).
Project
The project should cover one of the topics learned in this course. It could be focused on implementing a novel machine learning algorithm to a geoscientific problem or on performing a systematic comparison of different machine learning algorithms to a geoscientific dataset.
Students are encouraged to start the project early. The best way is to define a problem statement at the beginning of the term and learn how to use machine learning to solve such a problem during the course.
Collaboration
Most homeworks involve programming assignments. Students are encouraged to collaborate and consult with each other, but an individual assignments (and code) must be handed in. Acknowledge explicitly in your submitted assignment if you have collaborated with someone else while working on the assignment.
Late submissions
Each student has access to one late submission wildcard of no more than 2 days from the submission deadline. Apart from using this wildcard, late submissions will be penalized with a loss of 40% of the achieved score.