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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.
Unsupervised learning:
- Association rule analysis
- Clustering
Supervised learning:
- 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 | 25 |
Autonomous Student Learning | 60 |
Lectures | 24 |
Computer Aided Lab | 11 |
Total | 120 |
- 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 | 30 |
Examination: End of trimester written exam | 2 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 70 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Mr Brian Buckley | Tutor |
Mr Brian Hassett | Tutor |
Ms Iuliia Promskaia | Tutor |
Niyati Seth | 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 |