252-0220-00L Introduction to Machine Learning
|Semester||Spring Semester 2022|
|Lecturers||A. Krause, F. Yang|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact email@example.com|
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||8 credits|
|Examiners||A. Krause, F. Yang|
|Language of examination||English|
|Repetition||The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.|
|Mode of examination||written 120 minutes|
|Additional information on mode of examination||70% session examination, 30% project; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project must be passed on its own and has a bonus/penalty function.|
The practical projects are an integral part (60 hours of work, 2 credits) of the course. Participation is mandatory.
Failing the project results in a failing grade for the overall examination of Introduction to Machine Learning (252-0220-00L).
Students who do not pass the project are required to de-register from the exam and will otherwise be treated as a no show.
Exceptionally, an examination during the winter examination session 2023 will be offered.
|Written aids||Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.|
Simple non-programmable calculator
|Online examination||The examination may take place on the computer.|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|