1. Course Description
This course focuses on the application of Generative AI to create synthetic data for training, testing, and evaluating AI models in contexts where real-world data is scarce, sensitive, or difficult to share.
The course provides a comprehensive foundation, covering the role of data in AI, the challenges of real-world data, and the techniques for generating, evaluating, and applying synthetic data in modern AI systems, ensuring effectiveness, security, and compliance.
2. Learning Outcomes
Upon completion of the course, participants are expected to acquire the following knowledge and competencies:
• Understand the role of data and the need for synthetic data in artificial intelligence and machine learning (AI/ML) use cases
• Identify limitations of real-world data, including data scarcity, bias, and privacy risks, and understand how Generative AI addresses these challenges
• Apply Generative AI models to generate synthetic data for model training and testing
• Evaluate the quality, reliability, and privacy-preserving properties of synthetic data
• Apply synthetic data to real-world scenarios in enterprises and AI research
3. Course Structure and Key Modules
• The Role of Data in Artificial Intelligence
• Introduction to Generative AI
• Challenges of Real-World Data
• Synthetic Data
• Methods for Synthetic Data Generation
• Methods for Applying Generative AI to Synthetic Data Generation
• Evaluation of Synthetic Data
• Ethical Issues, Privacy Considerations, and Synthetic Data Risks
• Advanced Topics in Synthetic Data
4. Duration: 4 days per class
5. Certification Organization: The International Society of Data Scientists (ISODS)
