Postgraduate Programme and Module Handbook 2023-2024 (archived)
Module COMP42015: Learning from Data
Department: Computer Science
COMP42015: Learning from Data
| Type | Tied | Level | 4 | Credits | 15 | Availability | Available in 2023/24 | Module Cap | None. |
|---|
| Tied to | G5K709 |
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Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To equip students with conceptual and practical tools to support machine learning
Content
- Data exploration e.g. missing value treatment, outlier detection, feature engineering
- Machine learning including artificial neural networks
- Deep learning
Learning Outcomes
Subject-specific Knowledge:
- By the end of this module, students should:
- Have a critical appreciation of the importance of preparing data for machine learning
- Have an advanced understanding of modern approaches to machine learning
Subject-specific Skills:
- By the end of the module students should be able to:
- Select and implement appropriate methods for preparing a data set for machine learning
- Train a machine learning classification application based on real data
- Use deep learning architectures to enhance machine learning
Key Skills:
- Effective written communication
- Oral presentation
- Planning, organising and time-management
- Problem solving and analysis
- Team working
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Learning outcomes are met through classroom-based workshops, supported by online resources. The workshops consist of a combination of taught input, group work, case studies, discussion and computing labs. Online resources provide preparatory material for the workshops – typically consisting of directed reading and video content.
- The summative assessments are an individual written assignment and a group presentation based on group work analysis of a real data set. These are designed to test students’ skills in problem identification, their theoretical understanding, and their ability to analyse the situation in order to categorise the potential solutions.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 5 | 1 a week in weeks 5-9 of term | 2 hours | 10 | ■ |
| Lectures | 5 | 1 a week in weeks 5-9 of term | 1 hour | 5 | ■ |
| Computer workshops (max 30 students) | 5 | 1 a week in weeks 5-9 of term | 2 hours | 10 | ■ |
| Preparation and reading | 125 | ||||
| Total | 150 |
Summative Assessment
| Component: Coursework | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Individual Written Assignment | 1500 words maximum | 50% | |
| Group presentation | 10 minutes | 50% | |
Formative Assessment:
The formative assessment consists of classroom-based exercises involving individual and group analyses and presentations on specific business situations/problems relevant to the learning outcomes of the module. Oral and written feedback will be given on a group and/or individual basis as appropriate.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University