Show/hide contentOpenClose All
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||
|Autonomous Student Learning||
- 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||Other||No||
|Examination: End of trimester written exam||2 hour End of Trimester Exam||No||Other||No||
|Resit In||Terminal Exam|
|Summer||Yes - 2 Hour|
• Feedback individually to students, post-assessment
Not yet recorded.
|Mr John O'Sullivan||Tutor|