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COMM416DA - Learning From Data (Professional) (2023)
MODULE TITLE | Learning From Data (Professional) | CREDIT VALUE | 15 |
---|---|---|---|
MODULE CODE | COMM416DA | MODULE CONVENER | Unknown |
DURATION: TERM | 1 | 2 | 3 |
---|---|---|---|
DURATION: WEEKS | 0 | 0 | 11 |
Number of Students Taking Module (anticipated) | 90 |
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- Taxonomy of problems and approaches in machine learning and statistical modelling
- Data description and pre-processing
- Probabilistic classification
- Clustering and dimension reduction
- Linear and logistic statistical models
- Model assessment, cross-validation, hypothesis
- Testing Bayesian learning
- Linear support vector machines
- Clustering (hierarchical and partitional)
- Principal component analysis
Scheduled Learning & Teaching Activities | 40.00 | Guided Independent Study | 110.00 | Placement / Study Abroad | 0.00 |
---|
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time) |
|||||
Scheduled Learning & Teaching Activities |
40 |
Guided Independent Study |
110 |
Placement / Study Abroad |
0 |
Form of Assessment |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
Feedback on practical work |
12 hours |
1-8 |
Oral |
Coursework | 80 | Written Exams | 20 | Practical Exams | 0 |
---|
Form of Assessment |
% of Credit |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
Written exam |
20 |
6-8 multiple-choice questions |
1-8 |
Written |
Individual technical report |
80 |
3000 words |
1-10 |
Written |
Original Form of Assessment |
Form of Re-assessment |
ILOs Re-assessed |
Time Scale for Re-assessment |
In Class Test |
Written exam |
1 |
Within 8 weeks |
Individual technical report |
Individual technical report |
2-10 |
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 reassessment 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 | Search |
---|---|---|---|---|---|---|---|
Set | Haykin, S | Neural Networks: A Comprehensive Foundation | 2nd | Pearson | 1999 | 000-013-908-385-3 | [Library] |
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 | 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 |
---|---|---|---|
PRE-REQUISITE MODULES | COMM415DA |
---|---|
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 | Tuesday 24 January 2023 |
KEY WORDS SEARCH | data science, machine learning, statistical modelling, data visualisation |
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