Learning from noisy data

Project Level: Summer

Project Duration: 6 weeks

Hours of Engagement: 20-36 hours

Project Description:

Machine learning algorithms often assume data is correctly labelled, but in practice the labelling process is often error-prone, and it is reported that the ratio of corrupted labels range from 8%-38.5% in various real-world datasets. This project investigates the robustness of various machine learning algorithms against label noise, and aims to develop highly robust machine learning algorithms.

Expected Outcomes:

• Develop a general understanding of general robust approaches for learning from noisily labelled data

• Develop empirical and/or theoretical understanding of the robustness of various machine learning algorithms, and develop new robust machine learning algorithms

• Develop skills in research design, implementation, experimentation, and communication.

• A report documenting the work done and the findings.

Suitable for:

Essential: knowledge of machine learning, strong programming skills.

Desirable: knowledge of deep learning

Contact for further information:

Dr Nan Ye: nan.ye@uq.edu.au