Last modified: 27 Feb 2018 18:42
Artificial intelligence has helped solve complex practical problems such as driving a car, translating text from/to different languages, understanding and answering questions, and playing games such as chess and Go. This course will provide students of our MSc in AI with knowledge of core data mining and visualisation approaches, tools, techniques and technologies.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Old Aberdeen | Sustained Study | No |
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This course aims to make students familiar with data mining and visualisation techniques and software tools. Students will learn how to analyse complex datasets by applying data pre-processing, exploration, clustering and classification, time series analysis, and many other techniques. This course will also cover text mining and qualitative modelling. Through this course students will be able to analyse real-world datasets in various domains and discover novel patterns from them. This course is particularly suitable for those who are interested in working as data analysts or data scientists in the future. Topics include: pattern detectors, treemaps, medical decision support and supporting analysis of genome data.
Information on contact teaching time is available from the course guide.
Examination Paper (50%); Continuous In-course Assessment: Analysis of datasets and write one report. (50%).
Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed. 1 two-hour written examination (75%) and continuous assessment mark (25%) where the mark for the passed part is carried forward.
There are no assessments for this course.
Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.
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