Edit StudentProjectForm: Explainable AI and poverty prediction

You do not have permission to edit this page, for the following reason:

The action you have requested is limited to users in the group: Users.

Summary:	MANDATORY: (provide a one-line* summary of the project)*
Programme:	OPTIONAL: (Name and the number of credits)
Keywords:	OPTIONAL: (Give 4-5 keywords)
TimeFrame:	MANDATORY: (Indicate time for the project, for example, Fall 2024)
References:	(1) Lee and Braithwaite (2021), "High-Resolution Poverty Maps in Sub-Saharan Africa", https://arxiv.org/abs/2009.00544 (2) Jean, Burke, Xie, Davis, Lobell, and Ermon (2016), "Combining satellite imagery and machine learning to predict poverty", Science, https://www.science.org/doi/10.1126/science.aaf7894 (3) Roscher, Bohn, Duarte, and Garcke (2020), "Explainable Machine Learning for Scientific Insights and Discoveries", IEEE Access, https://ieeexplore.ieee.org/document/9007737 OPTIONAL: (Give 2-3 references for students to start with)
Prerequisites:	Machine learning, deep learning OPTIONAL: (Courses, any other important information)
Supervisor(s):	MANDATORY: (can be multiple names, coma-separated)
Examiner:	OPTIONAL: (Name of project Examiner)
Author(s):	OPTIONAL: (can be multiple names, coma-separated)
Level:	MANDATORY: choose one, preferably <Master>
Status:	MANDATORY: choose <Open> when the topic is ready for students to select

Project description (free text)

Give a concise project description. Include:

Research question
Whether it’s more software or hardware related
A very brief description of 3-4 work packages
Deliverables/outcomes/results

The task is to first reproduce some of the results in Lee & Braithwaite (2021), then build further on that to explain what features/properties that are important for the predictions and also how. Data is available. This is a software project involving high volumes of data. The implementation of the model is not trivial and requires very good programming skills and understanding of machine learning tools. The work is done in cooperation with the Department of Human Geography, Lund University. WP 1: Literature study to understand and define the problem. WP 2: Reproduce the model in Lee & Braithwaite (2021) for a few countries. WP 3: Choose a few methods for feature importance analysis and "XAI" and apply them to this model. Ideal outcome: a study of a current and important topic that will form the basis for a scientific contribution.

Summary:

This is a minor edit Watch this page

Cancel

Edit StudentProjectForm: Explainable AI and poverty prediction

Navigation menu

Views

Personal tools

Search

Tools