MSCS3451

Description

This course explores the principles and techniques behind AI systems capable of generating new content, such as images, text, music, and more. The course covers key generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models like GPT. Students will explore how these models generate human-like text, understand context, and perform tasks such as translation, summarization, and sentiment analysis. In addition to generative models, the course introduces prompt engineering, reasoning, and AI tools essential for fine-tuning outputs and enhancing creativity. The course discusses topics like agentic software, autonomous AI agents, multiagent systems, semantic search, retrieval augmented generation (RAG), and vector databases, and how these concepts and technologies are utilized in the development of GenAI applications. Students will explore techniques to build AI agents and learn how reasoning is integrated into generative systems to create more sophisticated and context-aware outputs through chaining. Through hands-on projects, students will gain experience in fine-tuning and deploying LLMs for various real-world applications, preparing them for advanced AI roles.