Skip to main content

Unit information: Interactive AI Group Project in 2019/20

Please note: Due to alternative arrangements for teaching and assessment in place from 18 March 2020 to mitigate against the restrictions in place due to COVID-19, information shown for 2019/20 may not always be accurate.

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Interactive AI Group Project
Unit code COMSM0026
Credit points 30
Level of study M/7
Teaching block(s) Teaching Block 4 (weeks 1-24)
Unit director Dr. Schien
Open unit status Not open




School/department Department of Computer Science
Faculty Faculty of Engineering

Description including Unit Aims

The aim of the group project is to integrate and consolidate knowledge from the different components and it serves to put in practice the skills learnt in other units, whilst focusing the workload and strengthening the cohort. Students will work in groups (3-5 students with defined roles mimicking an industrial setting). Each group project will be supervised by one academic and one industry partner, who will act as a stakeholder. Y2 students will act as mentors, thus building up leadership skills. The group will deliver a working Interactive AI system combining a particular set of AI topics, which will change from year to year, adapting to students’ interests, suggestions from industry partners and recent progress in AI. Topic sets suggested by our partners include {Information Retrieval, Text Mining and Natural Language Processing} and {Personalisation, Recommender Systems and Networks}. In practice, the project consists of three stages.

  1. Set up. The students are presented with a set of best practices as provided by our industry partners to realistically recreate an AI team. This involves discussions on software design, code reviews, agile development and continuous integration. Additionally, the students will be introduced to the computational and data infrastructure
  2. Dive in. The students plan and estimate the duration of the tasks and work together as a team. Additionally, the students learn about the targeted AI topics
  3. Wrap up. This stage involves the final testing and deployment. The students present the project and get feedback from the stakeholder – that can still be incorporated into the final report.

Intended Learning Outcomes

Successful completion of this unit will enable students to:

  1. Specify a design as part of a team;
  2. Implement the design according to an agreed specification;
  3. Integrate various parts in order to deliver a working system;
  4. Assess the performance of the system through experimental evaluation;
  5. Document the system in a written report, justifying all design choices made.

Teaching Information

Taught classes, group meetings and progress meetings

Assessment Information

Presentation (talk) involving the whole group, detailing the work done, way of working and break-down of tasks - 30%

Group Project Report, 20,000 words - 70%

Both assessments cover all ILO's

Reading and References

Dependent on project