Applied AI: AI for Science, Robotics, Product Strategy, Healthcare

About the course

The course covers AI applications in specialized domains: AI for Science (molecular modeling, climate), Robotics (embodied AI, sim-to-real transfer), Healthcare AI (regulatory requirements, clinical trials), and AI Product Strategy (ROI, build vs. buy, risk modeling). Students choose one track for in-depth study. The course builds the ability to work at the intersection of AI and a specific domain — a key skill for a career in applied AI.

What you'll learn

✓Apply specialized AI architectures within the context of a chosen domain

✓Assess the regulatory and ethical constraints of deploying AI in real-world systems

✓Build a business case for an AI initiative, including risk analysis and ROI

Key topics

•AI for Science: AlphaFold-like architectures, equivariant networks, neural ODEs

•Robotics: RL for control, imitation learning, the sim-to-real gap

•Foundation models for robotics: RT-2, Octo

•Healthcare AI: regulatory frameworks (FDA SaMD, CE marking), bias in clinical data

•AI Product Strategy: AI value chain, make-or-buy, AI risk taxonomy

•ROI assessment for AI products and unit economics

•MLOps in regulated industries: audit trails, model cards

•Responsible deployment: fairness, consent, data governance

This description was generated automatically and has not yet been reviewed by an instructor — it's a draft for discussion.

About the course

What you'll learn

Key topics

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