| Unit name | Applied Deep Learning (Teaching Unit) |
|---|---|
| Unit code | COMSM0045 |
| Credit points | 0 |
| 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 |
COMSM30035 Machine Learning (Teaching Unit) or equivalent. |
| Co-requisites |
EITHER Assessment Units COMSM0043 Applied Deep Learning (Exam assessment, 10 credits) OR COMSM0044 Applied Deep Learning (Coursework assessment, 15 credits). Please note: COMSM0046 is the Teaching Unit for the Applied Deep Learning option. Single Honours Computer Science and some Joint Honours students can choose to be assessed by either examination (10 credits, COMSM0043) or coursework (15 credits, COMSM0098) by selecting the appropriate co-requisite assessment unit. Any other students that are permitted to take the Applied Deep Learning option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMSM0043). |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
The unit introduces the students to the latest deep architectures,as well as learning approaches to optimising these for a variety of classification andregression problems. The unit paves the path from understanding the fundamentals of convolutional and recurrent neural networks through to trainingand optimisation as well as evaluation of learnt outcomes. The unit’s approach is hands-on, focusing on the ‘how-to’ while covering the basic theoretical foundations.
On successful completion of this unit, students will be able to:
When assessed by Examination, theoretical understanding gained from lectures and practical labs will be assessed in a written exam.
When assessed by Coursework, practical understanding from lectures and lab will be assessed by a coursework of replicating a published paper and a report written in groups of 2-3 students.
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.
Teaching will take place over Weeks 1-7, with coursework support in weeks 9-11 and for students assessed by examination, consolidation and revision sessions in Weeks 12.
Examination details:
2 hour exam (100%, 10 credits)
OR
Coursework details:
Coursework (100%, 15 credits) - to be completed during a specific period.
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. COMSM0045).
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.
