Applied AI summer school

Give students a rigorous but hands-on introduction to modern AI while cultivating ethical judgment, security awareness, and the ability to turn ideas into validated, pitch-ready solutions for public and private sector use cases.
Learning outcomes: By the end of the course, students will be able to:
1. Explain key AI paradigms (ML, NLP/LLMs, agents) and their limits using real examples.
2. Apply ML workflows and justify metric choices for a given use case.
3. Design and prototype an LLM- or agent-based workflow with basic guardrails and evaluation steps.
4. Analyze human–AI interaction needs and iterate a simple UX that supports human-in-the-loop decisions.
5. Evaluate ethical risks (bias, transparency, accountability) and document mitigations for their project.
6. Interpret core legal constraints (data/IP/liability) and prepare a brief compliance and data-governance note.
7. Identify security threats (e.g., prompt-injection, data leakage) and construct a minimal risk register & hardening plan.
8. Assess environmental impacts of their approach and recommend at least two sustainability improvements.
9. Compare societal impacts across sectors and propose a resilience workflow for a digital public service.
10. Synthesize a value proposition and deliver a concise, evidence-backed pitch (with Q&A) for a public or private use case.