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    Course Outline
Introduction to Energy-Efficient AI
- The significance of sustainability in AI
 - Overview of energy consumption in machine learning
 - Case studies of energy-efficient AI implementations
 
Compact Model Architectures
- Understanding model size and complexity
 - Techniques for designing small yet effective models
 - Comparing different model architectures for efficiency
 
Optimization and Compression Techniques
- Model pruning and quantization
 - Knowledge distillation for smaller models
 - Efficient training methods to reduce energy usage
 
Hardware Considerations for AI
- Selecting energy-efficient hardware for training and inference
 - The role of specialized processors like TPUs and FPGAs
 - Balancing performance and power consumption
 
Green Coding Practices
- Writing energy-efficient code
 - Profiling and optimizing AI algorithms
 - Best practices for sustainable software development
 
Renewable Energy and AI
- Integrating renewable energy sources in AI operations
 - Data center sustainability
 - The future of green AI infrastructure
 
Lifecycle Assessment of AI Systems
- Measuring the carbon footprint of AI models
 - Strategies for reducing environmental impact throughout the AI lifecycle
 - Case studies on lifecycle assessment in AI
 
Policy and Regulation for Sustainable AI
- Understanding global standards and regulations
 - The role of policy in promoting energy-efficient AI
 - Ethical considerations and societal impact
 
Project and Assessment
- Developing a prototype using small language models in a chosen domain
 - Presentation of the energy-efficient AI system
 - Evaluation based on technical efficiency, innovation, and environmental contribution
 
Summary and Next Steps
Requirements
- Solid understanding of deep learning concepts
 - Proficiency in Python programming
 - Experience with model optimization techniques
 
Audience
- Machine learning engineers
 - AI researchers and practitioners
 - Environmental advocates within the tech industry
 
             21 Hours