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ECMM458 - Machine Learning (Professional) (2023)
MODULE TITLE | Machine Learning (Professional) | CREDIT VALUE | 15 |
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MODULE CODE | ECMM458 | MODULE CONVENER | Dr Fabrizio Costa (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 0 | 0 |
Number of Students Taking Module (anticipated) | 90 |
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In this data-driven era, modern technologies are generating massive and high-dimensional datasets. This module aims to give you an understanding of computational methods used in modern data analysis. In particular, this module aims to impart knowledge and understanding of machine learning methods from basic pattern-analysis methods to state-of-the-art research topics; to give you experience of data-modelling development in practical workshops. Neural Networks, Bayesian methods and kernel-based algorithms will be introduced for extracting knowledge from large data sets of patterns (data mining techniques) where it is important to have explicit rules governing machine learning and pattern recognition. Recent development of techniques and algorithms for big-data analysis will also be addressed.
Scheduled Learning & Teaching Activities | 30.00 | Guided Independent Study | 40.00 | Placement / Study Abroad | 0.00 |
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Category | Hours of study time | Description |
Scheduled learning and teaching activities | 20 | Lectures |
Scheduled learning and teaching activities | 10 | Workshops/practicals |
Guided independent study | 20 | Coursework preparation |
Guided independent study | 20 | Wider reading and self study |
Coursework | 100 | Written Exams | 0 | Practical Exams | 0 |
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Form of Assessment |
% of Credit |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
Coursework (1 piece) |
100 |
2000-3500 words per piece |
All |
Written |
Original Form of Assessment |
Form of Re-assessment |
ILOs Re-assessed |
Time Scale for Re-assessment |
Coursework |
Coursework |
All |
Wtihin 8 weeks |
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 | Search |
---|---|---|---|---|---|---|---|
Set | Christopher Bishop | Pattern Recognition and Machine Learning | Springer | 2007 | 978-0387310732 | [Library] | |
Set | Webb, A. | Statistical Pattern Recognition | 2 | Wiley | 2002 | 0-470-84513-9 | [Library] |
Set | Shawe-Taylor, J. and Cristianini, N. | Kernel methods for pattern analysis | Cambridge University Press | 2006 | 521813972 | [Library] | |
Set | Murphy, K. | Machine Learning: A Probabilistic Perspective | 1st | MIT Press | 2012 | 978-0-262-018029 | [Library] |
Set | David Barber | Bayesian Reasoning and Machine Learning | Cambridge University Press | 2012 | 978-0-521-51814-7 | [Library] | |
Set | Hastie T., Tibshirani R. & Friedman J. | The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 2nd | Springer | 2009 | 978-0387848587 | [Library] |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | ECMM431 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Tuesday 06 August 2019 | LAST REVISION DATE | Wednesday 18 January 2023 |
KEY WORDS SEARCH | Machine learning, statistical modelling |
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