Course Synopsis

  Background & Context

This course provides training in Artificial Intelligence and Machine Learning applied to disease surveillance. It aims to give participants the skills needed for future involvement in projects utilizing AI and Machine Learning in the health sector, with a focus on One Health applications.

  Objectives

• Explain what artificial intelligence is and identify its various domains
• Describe the main concepts of Machine Learning and identify different types and their applications
• Understand the Machine Learning model development process including classification and regression
• Learn key Machine Learning algorithms
• Introduction to data collection techniques and data processing
• Introduction to Python/R programming for Machine Learning
• Apply Machine Learning models for animal health surveillance
• Understand applications of Machine Learning in the One Health domain

  Target Audience

Graduate students and professionals interested in AI applications for health surveillance and One Health.

  Prerequisites

No mandatory prerequisites. Course notes, references and additional readings will be provided.

  Recommended Readings

• Russell S., Norvig P., Artificial Intelligence: A Modern Approach. Prentice-Hall, 2009
• Alpaydin E., Introduction to Machine Learning, MIT Press, 2010
• Koller D., Friedman N., Probabilistic Graphical Models, MIT Press, 2009
• El-Sofany, H. et al. (2024) — A proposed technique for predicting heart disease using ML algorithms. Scientific Reports, 14, 23277