23EET401 GENERATIVE AI AND AGENTIC AI FOR ELECTRICAL ENGINEERS

This course introduces Electrical and Electronics Engineering students to the rapidly evolving fields of Generative AI and Agentic AI systems, with a strong focus on practical applications in power systems, electronics design, smart grids, and related domains. Students will explore the foundations of Generative AI versus discriminative models, master prompt engineering techniques (zero-shot, few-shot, chaining), and study core neural generative architectures including Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).

The curriculum progresses to Large Language Models (LLMs) like the GPT family, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) for t**l use, multimodal integration (text, images, sensor data), and the development of AI agents with planning, reasoning, and function-calling capabilities. Special emphasis is placed on explainable AI (LIME, SHAP), fairness, safety, hallucination mitigation, and responsible deployment. EE-specific applications include AI-powered PCB layout, transformer/motor design, power flow analysis, load forecasting, EV charging infrastructure, microgrid management, and predictive maintenance.

Level: The course is designed for intermediate learners. It assumes foundational knowledge of programming, basic machine learning concepts, and electrical engineering principles, making it ideal for undergraduate or early postgraduate students seeking to bridge traditional EE with cutting-edge AI technologies.