| 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 | Dr. Wray |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
COMSM30035 Machine Learning (Teaching Unit) or equivalent. |
| Units you must take alongside this one (co-requisite units) |
EITHER Assessment Units COMSM0043 Applied Deep Learning (Examination assessment, 10 credits) OR COMSM0138 Applied Deep Learning (Examination and Coursework assessment, 20 credits). Please note: COMSM0043 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 examination and coursework (20 credits, COMSM0138) 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). |
| Units you may not take alongside this one |
None. |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
The unit introduces the students to the latest deep architectures, as well as learning approaches to optimising these for a variety of classification and regression problems.
How does this unit fit into your programme of study
This is an optional unit that can be taken in Year 4.
An overview of content
The unit paves the path from understanding the fundamentals of previously and currently used deep learning methods (such as multi-layer perceptrons and convolutional neural networks) through to training and optimisation as well as evaluation of learnt outcomes. The unit’s approach is hands-on, focusing on the ‘howto’ while covering the basic theoretical foundations
How will students, personally, be different as a result of the unit
Students will be aware of state of the art of Deep Learning and understand the benefits & limitations to these methods, and how these methods can be applied to a variety of machine learning problems.
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. Explain and discuss the theoretical underpinnings behind deep learning architectures and the methods in which they are trained
When the unit is taken with the associated 20 credit option that includes coursework, students will also 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, discuss and employ 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.
The unit includes lectures and a series of applied labs that allow students to learn the practical aspects of training deep learning methods. These labs include contact time with Teaching Assistants but can also be completed asynchronously. Given the cutting-edge nature of the content of this unit, this gives the best combination of students dictating their own learning with support from experts within the field. If taken with coursework, the unit also provides weekly coursework support sessions.
Tasks which help you learn and prepare you for summative tasks (formative):
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.
The unit begins with a series of lectures which covers the theoretical aspects of deep learning and its history with practical labs running alongside them once the basics have been covered. These practical labs have been designed to explore the theoretical content of the unit that is both beneficial for both the exam and coursework unit content.
Tasks which count towards your unit mark (summative):
2 hour exam (10 credits: COMSM0043 - 100%; COMSM0138 – 50%)
n addition, students taking COMSM0138 will also take a coursework, completed in groups of 2-3 students in weeks 9-11 (50%, equiv to 10 credits).
When assessment does not go to plan
Students will retake relevant assessments in a like-for-like fashion in accordance with the University rules and regulations.
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 University 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. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
