This course explores the theory and application of generative AI, focusing on the differences between stochastic AI, expert systems, and symbolic AI. You will learn how symbolic AI can be generative and how both stochastic and symbolic approaches can be integrated. Emphasis is placed on creating holistic, responsible AI solutions. Through practical examples, you will gain a deep understanding of AI's capabilities and ethical considerations.

This module explores the fundamentals and applications of Large Language Models (LLMs) and Transformers. It covers the foundations, capabilities, and fine-tuning of LLMs like ChatGPT, as well as their use in image generation. The module also addresses challenges such as hallucinations, vulnerabilities, and model competence, providing a comprehensive understanding of LLMs and their real-world implications.

This module explores the intersection of symbolic and generative AI, focusing on how symbolic AI informs and enhances generative processes. Building on prior knowledge of generative AI, it integrates symbolic reasoning with stochastic models to create responsible AI solutions. Key topics include symbolic AI, formal methods, relational calculus, and data integration, essential for enabling systems to generate insights in diverse environments. The module emphasizes how combining rule-based reasoning with generative AI fosters explainable, transparent systems that align with ethical and regulatory standards.