Overview
Explore machine learning applications in One Health, addressing interconnected human, animal, and environmental health issues with Graham Taylor, a leading AI researcher and entrepreneur.
Syllabus
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- Introduction to One Health
-- Overview of One Health concept
-- Importance of interconnected health solutions
- Fundamentals of Machine Learning
-- Basic concepts and algorithms
-- Supervised vs. unsupervised learning
-- Introduction to neural networks
- Machine Learning Tools and Frameworks
-- Popular libraries (e.g., TensorFlow, PyTorch)
-- Data preprocessing techniques
- Machine Learning for Human Health
-- Predictive modeling in healthcare
-- AI in diagnostics and treatment recommendations
-- Case studies in disease outbreak prediction
- Machine Learning for Animal Health
-- Applications in veterinary diagnostics
-- Monitoring and predicting animal disease
-- Wildlife conservation with AI
- Machine Learning for Environmental Health
-- Environmental monitoring with AI
-- Predictive models for climate change impacts
-- AI-driven pollution tracking and management
- Integrating AI in One Health Approaches
-- Cross-disciplinary data integration
-- Challenges and solutions in data sharing
-- Case studies of One Health AI applications
- Ethical and Societal Implications of AI in One Health
-- Privacy issues and data protection
-- Fairness and bias in AI models
-- Regulatory considerations
- Hands-On Project
-- Data collection and preprocessing
-- Model selection and training
-- Evaluation and presentation of results
- Future Directions in AI for One Health
-- Emerging trends and technologies
-- Collaborative approaches and global initiatives
- Course Review and Conclusion
-- Recap of key concepts
-- Discussion on future career opportunities in AI and One Health
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On-Demand
