Recent years have seen the success of representation learning techniques in the form of generative AI, in domains such as vision and language, as well as learning from multi-modal data. Furthermore, we are now seeing a great deal of enthusiasm in turning these theoretical developments into agents and products that can solve real-world problems with little to no assistance from human. Therefore, in this course, we plan to provide participants with upskilling in the latest developments in generative AI — theory, agents, and products. We will bring together some of the world’s top researchers (from academia and industry) in representation learning and generative AI (both theory and applied), to cover the latest developments and the current state-of-the-art in these areas:

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• Advanced theoretical topics in representation learning (e.g., vision, language, multi-modal, …)

• Agentic AI (e.g., agentic reasoning and design patterns)

• Human+AI alignment

• Building Gen. AI products — from model.fit() to market.fit()

• Using SOTA foundation models (e.g., fine tuning, RAG, RLHF, prompt engineering, …)

• Application of large frontier models in applied domains (e.g., medicine, finance, education, …)