COMP50080 ML CRT Summer School

Academic Year 2023/2024

The aim of this module is to train students in the process of organising a scientific event. The ML CRT Summer School is an annual week-long event for all current cohorts. This is a central event in the centre's calendar encouraging teamwork and scholarly exchange. Activities at the annual Summer School will include:
● An annual datathon in which researchers work in teams on projects driven by datasets provided by industry partners.
● Keynote presentations from international and local experts.
● Presentations by ML-Labs researchers on their work.
● Short workshops on key research and transferable skills (e.g. data protection, research
ethics, presentation skills).
● Presentations and technology workshops from industry partners.
● Panel discussions on recent experience of ML-Labs students returning from placement and ML-Labs graduates.
● Poster sessions and other match-making activities where industry partners have an opportunity to meet potential hires and placement candidates.
● Sessions targeted at current postgraduate and undergraduate students for recruitment

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Curricular information is subject to change

Learning Outcomes:

At the end of this module, the student will be able to:
1. Organise scientific events for scholarly exchange.
2. Design a scientific programme.
3. Coordinator all stakeholders to ensure the program is executed within the required timeframe and resources.
4. Prepare training materials for the event.
5. Prepare scientific posters and branded materials.
6. Participate in scientific panel discussions.
7. Organise datathons.
8. Engage in discussions of cutting edge machine learning techniques.
9. Engage in discussions of the impacts of machine learning on society.

Indicative Module Content:

The key topics that will be involved in the Summer School module include:

-- Presentations on cutting-edge machine learning
-- Workshops on the social implications of machine learning
-- Presentations by PhD candidates within the centre
-- Workshops of critical skills for PhD resaerch
-- Group development activities such as hackathons and ideation sessions.

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

25
Autonomous Student Learning

100
Total

125
Approaches to Teaching and Learning:
This module will be primarily based on experiential learning and will utilises approaches such as peer and group work, workshops lectures, enquiry & problem-based learning, debates, and student presentations. 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: This modules assesses the students engagement with and organization of the ML-Labs Summer School. Throughout the Trimester n/a Pass/Fail Grade Scale No

100

Carry forward of passed components
No
 
Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Delivered after key stages within the module.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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