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COMP42015: Learning from Data

It is possible that changes to modules or programmes might need to be made during the academic year, in response to the impact of Covid-19 and/or any further changes in public health advice.

Type Tied
Level 4
Credits 15
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • None

Corequisites

  • None

Excluded Combinations 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

ActivityNumberFrequencyDurationTotalMonitored
Lectures51 a week in weeks 5-9 of term2 hours10Yes
Lectures51 a week in weeks 5-9 of term1 hour5Yes
Computer workshops (max 30 students)51 a week in weeks 5-9 of term2 hours10Yes
Preparation and reading125 
Total150 

Summative Assessment

Component: CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Individual Written Assignment1500 words maximum50 
Group presentation10 minutes50 

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.

More information

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