| Unit name | Advanced Visual AI (Teaching Unit) |
|---|---|
| Unit code | COMSM0159 |
| Credit points | 0 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Professor. Achim |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
COMS30030 Image Processing and Computer Vision (Teaching Unit) or equivalent COMS30035 Machine Learning (Teaching Unit) or equivalent |
| Units you must take alongside this one (co-requisite units) |
COMSM0045 Applied Deep Learning (Teaching Unit) or equivalent EITHER COMSM0158 Advanced Topics in Computer Science MINOR (Examination assessment, 20 credits) OR COMSM0160 Advanced Visual AI (Coursework & In-class test assessment, 20 credits). Please note: This unit is the Teaching only unit for the Advanced Visual AI option. Students taking this unit choose to be assessed by EITHER the MAJOR 20 credit unit (COMSM0158) OR as part of the Advanced Topics in Computer Science MINOR 20 credit examination unit. Students select the form of assessment to be taken by enrolling on the appropriate co-requisite assessment unit |
| Units you may not take alongside this one |
None |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
Visual AI drives innovation across various domains, including medical diagnostics, surveillance systems, animation, augmented reality, and other impactful applications. This unit equips you to tackle real-world challenges by comprehending and interpreting visual data. Beginning with a focus on the theoretical foundations of model-based and learning-based approaches for Visual AI, the unit then provides cutting-edge solutions for inverse problems, such as superresolution and style transfer. Additionally, it explores the latest generative AI techniques for visual data generation and delves into neural representations for shapes and videos. Through hands-on labs, you'll apply various tools and techniques to real research data, gaining practical experience in the field.
How does this unit fit into your programme of study
This is an optional unit that can be taken in Year 4. This positioning allows students to make use of fundamental skills and knowledge developed during the first 3 years of their study to learn about the state-of-the-art in AI applied to image and videos. This unit will also support the CS with AI programme.
An overview of content
The unit will inform students with a combination of theory and intensive practice in modern visual AI. From a technological perspective, students will learn:
How will students, personally, be different as a result of the unit
As a result of this unit, students will be aware of state of the art of AI-based methods for visual data and understand the benefits & limitations to these methods, and how these methods can be applied to a variety of real-world problems.
Learning Outcomes
On successful completion of this unit, ALL students (both MAJOR and MINOR) will be able to:
1. Identify the opportunities and challenges that AI brings to visual data, i.e. images and videos
2. Describe, setup, train and evaluate deep architectures on public datasets.
3. Explain and discuss the theoretical underpinnings behind AI-based methods.
When the unit is taken as the MAJOR 20 credit variant, students will also be able to:
4. Replicate published experiments from published academic papers.
5. Demonstrate ability to discuss, select, relate and apply newer and current approaches.
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, problem sheets and practical activities. 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 and students are encouraged to explore the wider literature. 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 as a MAJOR option, 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 1-8, 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 visual AI 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. Labs include opportunities for students to discuss and check their progress on the learning outcomes of the unit. For students taking the MAJOR unit, code developed during the labs will be a foundation of their final summative assessment.
Tasks which count towards your unit mark (summative)
For students taking this unit with the Advanced Topics in Computer Science (MINOR) examination unit, it will contribute 50% towards the 20cp Advanced Topics in Computer Science exam, (equivalent to 1 hour of exam time) that will be sat during the winter examination period. This closed-book exam will assess Learning Outcomes 1, 2, and 3.
For students taking this unit as a MAJOR variant, there will be two elements of assessment:
- A mid-term in-class test that will assess Learning Outcomes 1, 2, 3, (worth 30% of the unit) - An end-of-term coursework (programming exercise + written report), (taking place during weeks 9-11) that will assess Learning Outcomes 3, 4 and 5 (worth 70% of the unit)
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. COMSM0159).
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 assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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.
