The objective of this course is to build capacity among policymakers and stakeholders to better understand their science, technology, and innovation (STI) ecosystems and thus design and implement successful STI policies. The 2026 spring-summer session is closed for registration. The registration deadline was 20 February 2026. A new term and intake will be announced in Q4 2026.

This virtual course provides an introduction to how behavioral science impacts policy design, enhancing outcomes in various sectors, including education, healthcare, social norms, gender equality, tax compliance, climate change, and more.

This course introduces participants to blockchain technology and provides a basic understanding of its main features. It begins by highlighting the business value of blockchain and the relevance of the technology for international trade. The lectures then move to explain the difference between blockchain and distributed ledger technology (DLT), defining key use cases for digital identity (ID), supply chain management, transportation / logistics, intellectual property rights, trade facilitation and financial services. The lectures further examine the opportunities blockchain / DLT may create for small producers and enterprises, especially those located in developing countries. Finally, the course explores the “blockchain for trade” ecosystem, implementation decision-making tools, regulatory requirements and other challenges that must be addressed before the technology can be used on a wide scale and generate significant impact.

By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners