This module will develop machine learning skills in R through techniques such as: principal component analysis, factor analysis, clustering, classification (e.g., CART and random forests), simulation and sampling, support vector machines. There will be a focus on teamwork and collaborative working, including version control using platforms such as GitHub. RMarkdown or similar software for producing reports will be used. Ethical implications will be discussed throughout.
Private Study: 170
Contact Hours: 30
Total: 200
Main assessment methods
Individual RShiny app or equivalent webpage – 40%
Group RMarkdown project – 60%
Reassessment methods
100% coursework
The most up to date reading list for each module can be found on the university's reading list pages.
On successfully completing the module students will be able to:
1) Demonstrate a comprehensive understanding of key machine learning techniques and apply them systematically.
2) Apply machine learning techniques such as principal component analysis, factor analysis, clustering, classification, simulation and sampling, and support vector machines systematically in R.
3) Demonstrate a comprehensive understanding of version control techniques using platforms such as GitHub and apply them systematically.
4) Use software such as RShiny and RMarkdown to communicate conceptual and practical understanding and results effectively.
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