The course 'Synthetic Data Generation for Finance' explores the use of deep learning techniques, including Generative Adversarial Networks (GANs), to create synthetic financial data indistinguishable from real-world data. Throughout this course, doctoral candidates will learn the principles and methodologies behind synthetic data generation, focusing on techniques to replicate the patterns and statistical properties of authentic financial datasets. Candidates will explore various use cases for synthetic data in AI training, such as fraud detection, crisis simulation, and risk assessment, aimed to enhance the robustness and scalability of financial models. The course facilitates practical experience in implementing GANs and other deep learning models to generate synthetic data tailored to specific financial applications. The course develops the skills and insights to leverage synthetic data effectively in financial analysis, decision-making, and AI-driven innovations, unlocking new opportunities for experimentation and development in the field of finance.