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Unit information: Applied Deep Learning (Teaching Unit) in 2021/22

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 Department of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

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.

Intended Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Identify the opportunities and challenges that deep architectures, both convolutional and recurrent, bring to machine learning tasks such as classification and regression.
  2. Understand and discuss the role of a variety of optimization approaches in parameter training for deep architectures.
  3. Setup, train and evaluate deep architectures on public datasets.
  4. Replicate published experiments and discuss the successes and failures of current architectures.

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 Information

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.

Assessment Information

Examination details:

2 hour exam (100%, 10 credits)

OR

Coursework details:

Coursework (100%, 15 credits) - to be completed during a specific period.

Resources

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.

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