Study information

Learning from Data - 2025 entry

MODULE TITLELearning from Data CREDIT VALUE15
MODULE CODEECMM445 MODULE CONVENERDr Diogo Pacheco (Coordinator), Dr Chico Camargo
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content
Artificially intelligent machines and software must assimilate data from their environment and make decisions based upon it. Likewise, we live in a data-rich society and must be able to make sense of complex datasets. This module will introduce you to machine learning methods for learning from data. You will learn about the principal learning paradigms from a theoretical point of view and gain practical experience through a series of workshops. Throughout the module, there will be an emphasis on dealing with real data, and you will use, modify and write software to implement learning algorithms. It is often useful to be able to visualise data and you will gain experience of methods of reducing the dimension of large datasets to facilitate visualisation and understanding.
 
The module will also cover some recent neural network architectures and related learning algorithms.
AIMS - intentions of the module

This module aims to equip you with the fundamentals of machine learning and at the same time discuss technical aspects of some well-known machine learning models and related learning algorithms. It will provide a thorough grounding in the theory and application of machine learning and statistical techniques for classification, regression and unsupervised methods (clustering and dimension reduction). The module will cover kernel methods and neural networks (feed-forward architectures only).

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:
 
Module Specific Skills and Knowledge
 
1. Apply principles for statistical and neural pattern recognition to novel data.
2. Analyse novel pattern recognition and classification problems, establish models for them and write software to solve them.
 
Discipline Specific Skills and Knowledge
 
3. Utilise a range of supervised and unsupervised pattern recognition and machine learning techniques to solve a wide range of problems.
4. State the importance and difficulty of establishing principled models for pattern recognition.
 
Personal and Key Transferable / Employment Skills and Knowledge
 
5. Use Python or other programming languages for scientific analysis and simulation.
6. Identify the compromises and trade-offs that must be made when translating theory into practice.
7. Critically read and report on research papers.

 

SYLLABUS PLAN - summary of the structure and academic content of the module
Topics (with associated exercises and seminar discussions):
  • Taxonomy of problems and approaches in machine learning and statistical modelling
  • Supervised Learning – Classification and Regression
    • Decision tree.
    • Similarity-based Learning.
    • Error based learning.
    • Neural Network concepts.
    • Ensemble learning concepts.
    • Model and classifier evaluation.
  • Unsupervised Learning
    • Clustering: hierarchical, partitional and density based.
    • Cluster Evaluation.
    • Association Rules.
  • Data description and pre-processing
    • Dealing with lass and imbalance and resampling.
    • Missing values and imputation.
    • Noise and Outlier Detection
    • Feature Selection
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 42 Guided Independent Study 108 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 22 Lectures
Scheduled learning and teaching activities 20 Workshops/tutorials
Guided independent study 50 Individual assessed work
Guided independent study 58 Private study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Feedback on practical work 12 hours All Oral
MCQ mock quiz 1 hour 1-4, 6-7 Online

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam 70 2 hours 1-4, 6-7 Oral on request
Coursework/Project 30 4,000 words All Written

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Written exam Written exam (2 hours, 70%) 1-4, 6-7 Referral/deferral period
Coursework / project Coursework / project (4,000 words, 30%) All Referral/deferral period

 

RE-ASSESSMENT NOTES
Reassessment will be by coursework and/or exam (containing multiple choice questions and open-ended questions) in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
 
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
 
• Duda and Hart, Pattern Classification and Scene Analysis, 2nd, Wiley, 2002, ISBN 0471056693
• Webb, A., Statistical Pattern Recognition, 2nd, Wiley, 2002, 0-470-84513-9
• Murphy, K., Machine Learning: A Probabilistic Perspective, MIT Press, 2012, ISBN 978-0-262-018029
• John C. Bishop, Pattern recognition and machine learning, Springer, 2006
• Haykin, S., Neural Networks and Learning Machines, 3rd, Pearson, Prentice Hall, ISBN 978-0-13-14713-9
 
Web-based and electronic resources: 
 
• ELE 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Duda and Hart Pattern Classification and Scene Analysis 2nd Wiley 2002 0471056693
Set Webb, A. Statistical Pattern Recognition 2 Wiley 2002 0-470-84513-9
Set Murphy, K. Machine Learning: A Probabilistic Perspective 1st MIT Press 2012 978-0-262-018029
Set Bishop, John C. Pattern recognition and machine learning Springer 2006
Set Haykin, S. Neural Networks and Learning Machines 3 Pearson, Prentice Hall 978-0-13-14713-9-
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 14th March 2024 LAST REVISION DATE Wednesday 7th May 2025
KEY WORDS SEARCH Data; machine learning; pattern recognition; probability.

Please note that all modules are subject to change, please get in touch if you have any questions about this module.