252-0220-00L Introduction to Machine Learning
| Semester | Spring Semester 2019 |
| Lecturers | A. Krause |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Previously called Learning and Intelligent Systems. |
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 8 credits |
| Examiners | A. Krause |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| 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. Die Prüfung kann am Computer stattfinden / The exam might take place at a computer. 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. |
| Written aids | Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size. |
| Digital exam | The exam takes place on devices provided by ETH Zurich. |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |