DM566: Data mining and machine learning (10 ECTS)
STADS: N330039101Level
Teaching period
Teacher responsible
No responsible teachers found, contact the department if necessary
Timetable
| Group | Type | Day | Time | Classroom | Weeks | Comment |
|---|---|---|---|---|---|---|
| Common | I | Monday | 08-10 | U46 | 6,8-9,11,13-14 | |
| Common | I | Monday | 08-10 | U23 | 7 | |
| Common | I | Monday | 08-10 | U20 | 10,12 | |
| Common | I | Monday | 08-10 | U110 | 17-18 | |
| Common | I | Tuesday | 08-10 | U110 | 5 | |
| Common | I | Thursday | 10-12 | U150 | 5-6 | |
| Common | I | Thursday | 10-12 | U20 | 7 | |
| Common | I | Thursday | 15-17 | U20 | 8 | |
| Common | I | Thursday | 15-17 | U48A | 9-12 | |
| Common | I | Thursday | 15-17 | U110 | 13 | |
| Common | I | Thursday | 15-17 | U150 | 17 | |
| Common | I | Thursday | 10-12 | U46 | 18 | |
| H1 | TE | Tuesday | 12-14 | U21 | 6,16,21-22 | |
| H1 | TE | Tuesday | 10-12 | 7 | ||
| H1 | TE | Tuesday | 10-12 | U151 | 8-10 | |
| H1 | TE | Tuesday | 10-12 | U154 | 11-14,17-20 | |
| H1 | TE | Wednesday | 14-16 | U21 | 21-22 | |
| H1 | TE | Thursday | 14-16 | U21 | 16 | |
| H2 | TE | Monday | 08-10 | U155 | 22 | |
| H2 | TE | Tuesday | 14-16 | U155 | 20 | |
| H2 | TE | Wednesday | 10-12 | U155 | 16,21 | |
| H2 | TE | Wednesday | 10-12 | U146 | 17 | |
| H2 | TE | Wednesday | 16-18 | U155 | 19 | |
| H2 | TE | Thursday | 14-16 | U21 | 6-7 | |
| H2 | TE | Thursday | 13-15 | U27A | 8 | |
| H2 | TE | Thursday | 13-15 | U28 | 9-11,13-14 | |
| H2 | TE | Thursday | 14-16 | U155 | 18 | |
| H2 | TE | Friday | 08-10 | U21 | 12 | |
| H2 | TE | Friday | 10-12 | U21 | 16 | |
| H3 | TE | Tuesday | 14-16 | U21 | 19 | |
| H3 | TE | Wednesday | 12-14 | U152 | 16 | |
| H3 | TE | Wednesday | 12-14 | U21 | 21-22 | |
| H3 | TE | Friday | 12-14 | U21 | 6-10,12,14,16-18 | |
| H3 | TE | Friday | 14-16 | U21 | 11,13 | |
| H3 | TE | Friday | 10-12 | U21 | 20 |
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Prerequisites:
None
Academic preconditions:
None
Course introduction
Expected learning outcome
Subject overview
Literature
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Website
This course uses e-learn (blackboard).
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Assessment and marking:
Expected working hours
The teaching method is based on three phase model.
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