- Level: Beginner
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Introduction to AI and Climate Change
Learning Outcome: Understand how AI can address climate challenges.1.1 Overview of climate change issues.
1.2 Role of AI in sustainability.
1.3 Examples of AI in climate change.
Practical Component
Analyze climate datasets.
Week 2: Climate Data and AI Models
Learning Outcome: Work with climate data for predictions.1.4 Sources of climate data (satellites, sensors).
1.5 Data preprocessing techniques.
1.6 Building predictive models for climate patterns.
Practical Component
Predict temperature changes using AI.
Week 3: Renewable Energy Optimization
Learning Outcome: Optimize energy systems using AI.1.7 Forecasting renewable energy generation.
1.8 AI for efficient energy storage.
1.9 Balancing energy grids with AI.
Practical Component
Simulate energy optimization using AI.
Week 4: Monitoring and Early Warning Systems
Learning Outcome: Build AI systems for climate monitoring.1.10 AI for natural disaster prediction.
1.11 Monitoring deforestation with AI.
1.12 Detecting pollution levels using AI sensors.
Practical Component
Develop an early warning system for floods.
Week 5: Ethics and Challenges in AI for Climate
Learning Outcome: Address challenges in implementing AI for climate solutions.1.13 Managing biases in climate models.
1.14 Ensuring data quality and reliability.
1.15 Regulatory compliance for AI solutions.
Practical Component
Analyze challenges in AI for renewable energy.
Week 6: Final Project
Learning Outcome: Build and present an AI application for climate change.1.16 Define a climate challenge (e.g., pollution monitoring).
1.17 Develop and test the solution.
1.18 Present the project to peers.
Practical Component
Deliver a working AI model for climate monitoring.