Air Pollution – a Global Threat to our Health Syllabus

Overview: This course explores the critical intersection of air pollution and global public health, offering a comprehensive understanding of environmental factors impacting human well-being. Designed for learners interested in health science and environmental policy, the program blends case studies, interactive labs, and guided projects to build awareness of pollution-related health threats. With approximately 15-20 hours of content across six modules, the course emphasizes real-world applications and evidence-based analysis, culminating in a final project. Ideal for public health and environmental professionals seeking to address growing climate and health challenges.

Module 1: Foundations of Computing & Algorithms

Estimated time: 4 hours

• Introduction to key concepts in foundations of computing & algorithms
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 2: Neural Networks & Deep Learning

Estimated time: 1-2 hours

• Review of tools and frameworks commonly used in practice
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback

Module 3: AI System Design & Architecture

Estimated time: 3 hours

• Case study analysis with real-world examples
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice
• Assessment: Quiz and peer-reviewed assignment

Module 4: Natural Language Processing

Estimated time: 2 hours

• Hands-on exercises applying natural language processing techniques
• Guided project work with instructor feedback
• Assessment: Quiz and peer-reviewed assignment

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2-3 hours

• Case study analysis with real-world examples
• Review of tools and frameworks commonly used in practice
• Assessment: Quiz and peer-reviewed assignment
• Discussion of best practices and industry standards

Module 6: Deployment & Production Systems

Estimated time: 3-4 hours

• Interactive lab: Building practical solutions
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Basic understanding of computing concepts
• Familiarity with data analysis or environmental science
• Interest in public health or AI applications

What You'll Be Able to Do After

• Understand core AI concepts including neural networks and deep learning
• Evaluate model performance using appropriate metrics and benchmarks
• Implement intelligent systems using modern frameworks and libraries
• Build and deploy AI-powered applications for real-world use cases
• Apply computational thinking to solve complex engineering problems