| Unit name | Practice Projects in AI |
|---|---|
| Unit code | COMSM0151 |
| Credit points | 40 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Dr. de Menezes e Silva Filho |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Computer Science |
| Faculty | Faculty of Engineering |
Why is this unit important?
This unit will allow students to engage with application domains provided by CDT partners, directly applying tools and proficiencies they have learned in the programme. Each time the student participates in the unit, they will work in cross-cohort groups, applying an AI solution to a problem originating in a different field. The cross-cohort nature of the unit means that participating students from Years 2 and 3 will have a chance to engage with practical activities at a higher level of seniority, e.g. coordinating Year-1 students and communicating with stakeholders. The objective is to learn, through practice, the specific skills needed to conceptualise, design, carry-out, and deploy a particular AI-supported task. Students will disseminate the findings both in written and oral form.
How does this unit fit into your programme of study?
The application of the skills acquired in the programme and in the unit will prepare students for carrying out research throughout the remainder of their PhD studies, targeting specific problems in Practice-Oriented AI. The student will be assigned to projects based on their preferences. The student will follow this choice with extensive research around the subject, designing, planning and executing their project along with their group. It is expected that students will spend approximately 400 hours completing two group projects supported by regular supervision meetings.
An overview of content'
This is mostly a supervised group project, with some training sessions occurring alongside it. The problem is provided and introduced by one of the CDT partners. There will be 3-4 cross-cohort groups, each assigned an academic supervisor. The supervisors will support students to extend and complement their knowledge and skills gained from other units and activities on the programme to solve a particular task.
How will students, personally, be different as a result of the unit'
After completing this unit, students will be prepared to apply their knowledge in Practice-Oriented AI in different application areas. They will demonstrate their ability to motivate an AI problem or opportunity, understand the context of this problem, and address this AI problem or opportunity. This will prepare them for the task of shaping and changing the next generation of AI technologies being used worldwide.
Learning Outcomes'
Upon successful completion of this unit students will be able to:
Teaching will be delivered though regular supervision meetings involving discussion of progress, technical advice, and guidance. The projects will be conceived, designed, planned, and executed with the guidance of at least one academic supervisor and any representatives of the CDT partner that proposed the problem.
Tasks which help you learn and prepare you for summative tasks (formative):
Groups will have regular supervision meetings with their supervisor. They will also need to deliver regular reports covering the stages of development of an AI-supported project: requirements, design, dataset description, planning, prototyping, and deployment. These are all opportunities for formative feedback.
Tasks which count towards your unit mark (summative):
Students will work on two Practice Projects throughout the unit, one in each TB. For each project, a prototype will be developed and submitted as an online code repository along with any data gathered or generated and the appropriate code documentation. In addition, for each project the group will prepare a report of approximately 7,000 words (15 pages) documenting the approach followed and providing empirical evidence to what extent this solves the problem. Approach and outcomes will be further explored in an oral viva with the group. Finally, each student will submit an individual report detailing their reflection on and contribution to the project. Each of these four components (code repository, group report, group viva, individual report) will be assessed on a pass/fail basis. Passing the unit requires passing each component.
When assessment does not go to plan
In the case of required re-assessment, we would enable the student resitting to undertake further work and resubmission of the components as above in the form of a shorter individual project. At the discretion of the markers and Unit Director, if the student has not achieved the Learning Outcomes or has not provided sufficient evidence of achieving the Learning Outcomes, they will be required to re-sit the unit in the next Academic Year.
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. COMSM0151).
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.
