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Course Outline
Introduction to Generative AI for Government
- Defining generative artificial intelligence (AI)
- Overview of generative models, including Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)
- Applications and case studies relevant to government operations
The Need for Synthetic Data in Government
- Limitations of real data within public sector contexts
- Privacy and security concerns specific to governmental data
- Enhancing the robustness and reliability of AI models used by government agencies
Generating Synthetic Data for Government
- Techniques for synthetic data generation suitable for government use
- Ensuring data quality and diversity to meet public sector standards
- Practical workshop: Creating your first synthetic dataset for government applications
Evaluating Synthetic Data for Government
- Metrics for assessing the quality of synthetic data in governmental contexts
- Comparing the performance of synthetic versus real data in government AI models
- Case study analysis specific to government scenarios
Ethical and Legal Aspects for Government
- Navigating the ethical landscape within government operations
- Legal frameworks and compliance requirements for government data use
- Balancing innovation with responsibility in governmental AI applications
Advanced Topics in Data Synthesis for Government
- Synthetic data for unsupervised learning in government settings
- Cross-domain data synthesis tailored to government needs
- Future trends in generative AI for governmental applications
Capstone Project for Government
- Applying knowledge to real-world government scenarios
- Developing a synthetic data strategy for government agencies
- Assessment and feedback on governmental projects
Summary and Next Steps for Government
Requirements
- A foundational understanding of machine learning concepts
- Proficiency in Python programming
- Experience with data science workflows for government
Audience
- Data scientists
- Artificial intelligence practitioners
21 Hours
Testimonials (1)
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