Advanced Machine Learning - 2019 entry
MODULE TITLE | Advanced Machine Learning | CREDIT VALUE | 15 |
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MODULE CODE | ECMM436 | MODULE CONVENER | Unknown |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS |
Number of Students Taking Module (anticipated) | 6 |
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***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***
In this module, you will learn to analyse large and complex datasets (e.g. images, sequences), creating systems that adapt and improve over time to make predictions from data. You will learn about the most prominent and effective techniques currently employed in state-of-the-art machine learning systems: artificial neural network and mainifold learning. Practical exercises, individual study and group work will consolidate your learning.
Pre-requisite modules: ECMM4434 Machine Learning and Statistics.
Co-requisite modules: None.
This module is intended to advance your knowledge on the design of predictive systems. You will learn how to address classification tasks on complex data such as images, sequences or structured information. You will be introduced to predictive tasks that go beyond classification and regression, such as learning when only partial supervision information is available or when the concept being modelled is not constant in time.
The module will be delivered in an intensive one-week residential block, including lectures and practical work, followed by practical work during the rest of the term. Lectures will be accompanied by data analysis, algorithm implementation and seminar discussions. You will undertake individual coursework to develop predictive models to data of interest in your own organisation.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge
2. Demonstrate competence in handling and encoding image, sequential and non-standard data.
Discipline Specific Skills and Knowledge
5. Apply complex and advanced modelling techniques to a wide range of problems and domains.
Personal and Key Transferable / Employment Skills and Knowledge
7. Critically read research papers.
Topics will include:
Neural Networks:
- Perceptron algorithm and multi-layer perceptron
- Backpropagation
- Deep Learning
- Recursive Neural Networks
- Convolutional Neural Networks
- Auto-encoders
Kernel Methods:
- Support Vector Machines
- Kernel Principal Component Analysis and Kernel K-Means
- Kernels for structured data: sequences and graphs
Advanced Learning Problems:
- Semi-supervised Learning
- Transfer Learning
- Online Learning and Concept Drift
Scheduled Learning & Teaching Activities | 32 | Guided Independent Study | 118 | Placement / Study Abroad | 0 |
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Category | Hours of study time | Description |
Scheduled Learning & Teaching | 18 | Lectures |
Scheduled Learning & Teaching | 14 | Workshops/Tutorials |
Guided Independent Study | 34 | Project and Coursework |
Guided Independent Study | 88 | Guided Independent Study reading and coursework preparation |
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Daily Workshops | 4 hours per day | All | Oral |
Coursework | 50 | Written Exams | 0 | Practical Exams | 50 |
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Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Coursework | 50 | Project | All | Written |
In Class Presentation and Report | 50 | Report | All | Written |
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Coursework | Coursework | All | Within 8 weeks |
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
ELE: http://vle.exeter.ac.uk/
Web based and Electronic Resources:
Other Resources:
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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Set | Shawe-Taylor, J. and Cristianini, N. | Kernel methods for pattern analysis | Cambridge University Press | 2006 | 521813972 | |
Set | Christopher Bishop | Pattern Recognition and Machine Learning | Springer | 2007 | 978-0387310732 | |
Set | Webb, A. | Statistical Pattern Recognition | 2 | Wiley | 2002 | 0-470-84513-9 |
Set | Hastie, T., Tibshirani, R. & Friedman, J. | The Elements of Statistical Learning: Data Mining, Inference, and Prediction | 2nd | Springer | 2009 | 978-0387848570 |
Set | Murphy, K. | Machine Learning: A Probabilistic Perspective | 1st | MIT Press | 2012 | 978-0-262-018029 |
Set | David Barber | Bayesian Reasoning and Machine Learning | Cambridge University Press | 2012 | 978-0-521-51814-7 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | ECMM434 |
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CO-REQUISITE MODULES |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Thursday 6th July 2017 | LAST REVISION DATE | Wednesday 15th May 2019 |
KEY WORDS SEARCH | machine learning, statistics, complex data, artificial neural networks, kernel machines |
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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.