Learning from Data - 2025 entry
MODULE TITLE | Learning from Data | CREDIT VALUE | 15 |
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MODULE CODE | ECMM445 | MODULE CONVENER | Dr Diogo Pacheco (Coordinator), Dr Chico Camargo |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 0 | 0 |
Number of Students Taking Module (anticipated) | 30 |
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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).
- Taxonomy of problems and approaches in machine learning and statistical modelling
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Supervised Learning – Classification and Regression
- Decision tree.
- Similarity-based Learning.
- Error based learning.
- Neural Network concepts.
- Ensemble learning concepts.
- Model and classifier evaluation.
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Unsupervised Learning
- Clustering: hierarchical, partitional and density based.
- Cluster Evaluation.
- Association Rules.
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Data description and pre-processing
- Dealing with lass and imbalance and resampling.
- Missing values and imputation.
- Noise and Outlier Detection
- Feature Selection
Scheduled Learning & Teaching Activities | 42 | Guided Independent Study | 108 | Placement / Study Abroad | 0 |
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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 |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Feedback on practical work | 12 hours | All | Oral |
MCQ mock quiz | 1 hour | 1-4, 6-7 | Online |
Coursework | 30 | Written Exams | 70 | Practical Exams | 0 |
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Form of Assessment | % of credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Written exam | 70 | 2 hours | 1-4, 6-7 | Oral on request |
Coursework/Project | 30 | 4,000 words | All | Written |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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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 |
information that you are expected to consult. Further guidance will be provided by the Module Convener
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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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 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Thursday 14th March 2024 | LAST REVISION DATE | Wednesday 7th May 2025 |
KEY WORDS SEARCH | Data; machine learning; pattern recognition; probability. |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.