This project aims to understand the performance of  various ML algorithms over semi-structured data, that could alternatively be viewed as data with many missing variables.  The student will:

- Explore the real-world data,  representing field trials and historical data, aiming to make agricultural liming recommendations for NSW and ACT. The data is highly heterogeneous and sparse.

- Select some well-known and suitable ML methods appropriate for the data.

- Extract data from a Knowledge Graph  (KG) and prepare for selected methods.

- Apply and optimise methods to build predictive models for liming recommendations.

- Evaluate models.

- Compare and contrast performance results with novel and developing KG learning methods.

- Provide recommendations based on the evaluation to potentially improve the KG learning methods.

The student will work with a broader project team and will have access to the project's multi-GPU cluster server. The project is available  for up to 2 students, thus increasing the range of algorithms under consideration. It is most suitable for a 12-unit course, although other sizes are possible.