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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 Machine Learning and Artificial Intelligence methods introduced
- Being able to apply a range of Machine Learning and Artificial Intelligence methods, including Deep Learning
- Being able to evaluate the performance of the methods introduced, benchmarking them against each other based on out-of-sample prediction performance
- Use the statistical software R and Keras to implement these methods
Student Effort Type | Hours |
---|---|
Specified Learning Activities | 25 |
Autonomous Student Learning | 60 |
Online Learning | 35 |
Total | 120 |
- Knowledge and understanding of statistical machine learning theory and methods for supervised learning and classification, at a level equivalent to that which would be achieved upon completion of "Statistical Machine Learning STAT30270" (or STAT40750), or modules with similar contents and learning outcomes.
- Knowledge of data programming and data analysis at a level equivalent to that which would be achieved upon completion of "Data Programming with R STAT40620", and modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Knowledge of regression analysis and linear models, including multiple linear regression.
- Understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with standard probability distributions (Gaussian, Binomial, etc.).
- Knowledge and understanding of basic Bayesian inference would be 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
Not yet recorded.