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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||
|Autonomous Student Learning||
Knowledge and understanding of basic statistical machine learning theory and methods for supervised learning and classification (as from STAT30270 - STAT40750).
Familiarity with the R software for statistical computing and data programming.
|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|