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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.
Supervised learning:
- Logistic regression for classification
- Tree-based and ensemble methods
- Support vector machines
- Evaluation of classifiers, model selection, and tuning
Unsupervised learning:
- Clustering
- Matrix factorization
Other topics.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 11 |
Specified Learning Activities | 25 |
Autonomous Student Learning | 60 |
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 "STAT20100 Inferential Statistics" 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 "STAT30340 Data Programming with R", 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 | ||
---|---|---|---|---|---|
Assignment(Including Essay): The assignment may include a mix of exercises, questions, code-based exercises, data analysis tasks. | n/a | Other | No | 15 |
|
Exam (In-person): End of term exam. | n/a | 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 |
Iuliia Promskaia | Tutor |
Niyati Seth | Tutor |