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Curricular information is subject to change
On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the statistical learning methods introduced.
- Being able to use the different techniques according to the context and the purpose of analysis.
- Being able to evaluate the performance of the statistical learning methods introduced.
- Use the statistical software R to implement these methods and being able to interpret the relevant output.
- Association rule analysis
- Logistic regression for classification
- Classification trees
- Ensemble methods
- Support vector machines
- Evaluation of classifiers, model selection, and tuning
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
|Computer Aided Lab||
- Basic knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Basic understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with common probability distributions.
- Basic knowledge of regression analysis and linear models, including multiple linear regression.
- Familiarity with the R software for statistical computing and data programming.
- Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of "Inferential Statistics STAT20100" or "STAT30280 Inference for Data Analytics (Onl)", or modules with similar contents and learning outcomes.
- Students should have a knowledge of data programming and analysis at a level equivalent to that which would be achieved upon completion of "Data Programming with R STAT40620", and/or modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of regression analysis and linear models to a level equivalent to that of "STAT20230 Modern Regression Analysis" or "STAT20240 Predictive Analytics" is beneficial.
|Description||Timing||Component Scale||% of Final Grade|
|Continuous Assessment: Homework assignments, code-based exercises, data analysis tasks||Varies over the Trimester||n/a||Alternative linear conversion grade scale 40%||No||
|Examination: End of trimester written exam||2 hour End of Trimester Exam||No||Alternative linear conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Summer||Yes - 2 Hour|
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|Mr Brian Buckley||Tutor|
|Mr Brian Hassett||Tutor|
|Ms Iuliia Promskaia||Tutor|
|Lecture||Offering 1||Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33||Fri 11:00 - 11:50|
|Lecture||Offering 1||Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33||Wed 15:00 - 15:50|
|Computer Aided Lab||Offering 1||Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33||Thurs 17:00 - 17:50|
|Computer Aided Lab||Offering 2||Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33||Wed 17:00 - 17:50|
|Computer Aided Lab||Offering 3||Week(s) - 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33||Tues 17:00 - 17:50|