| Unit name | Applied Deep Learning |
|---|---|
| Unit code | COMSM0043 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Damen |
| Open unit status | Not open |
| Pre-requisites |
See COMSM0045 Applied Deep Learning (Teaching Unit). |
| Co-requisites |
COMSM0045 Applied Deep Learning (Teaching Unit). |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Please note, this is the 10 credit examination Assessment Unit for COMSM0045 Applied Deep Learning (Teaching Unit).
See COMSM0045 Applied Deep Learning (Teaching Unit).
See COMSM0045 Applied Deep Learning (Teaching Unit).
January timed assessment (100%).
See COMSM0045 Applied Deep Learning (Teaching Unit).
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. COMSM0043).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the Faculty workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. If you have self-certificated your absence from an
assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
