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 | 25 |
Autonomous Student Learning | 60 |
Online Learning | 35 |
Total | 120 |
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 | 30 |
Examination: End of trimester written exam | 2 hour End of Trimester Exam | No | Other | No | 70 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Feedback individually to students, post-assessment
Not yet recorded.