AI Techniques, Causal Inference & Business Optimization

This program equips data analysts and technical professionals to design AI solutions that stand up to real business scrutiny—measured, explainable, compliant, and optimized for impact. Across 11 hands-on short courses, you’ll build and evaluate conversational AI (including retrieval-augmented generation), explain black-box models for executive audiences, and move from descriptive analytics to prescriptive decision intelligence. You’ll also learn to diagnose operational problems with root-cause methods, apply modern optimization approaches (linear programming, mixed-integer methods, genetic algorithms, and reinforcement learning), and deploy real-time decision platforms that meet tight SLAs. The program rounds out with causal inference techniques to estimate true business impact, plus ethical AI, debiasing, privacy, and compliance practices to reduce risk and increase trust. Each course emphasizes practical deliverables, clear metrics (quality, fidelity, fairness, latency, robustness), and stakeholder-ready communication—so your work translates into measurable outcomes, not just model performance

• Build and evaluate LLM applications such as RAG chatbots and insight generation using clear quality and usability metrics
• Explain, govern, and de-risk models through XAI, fairness testing, privacy techniques, and compliance workflows
• Drive better decisions with causal inference and optimization methods including LP/MIP, GA, and RL, deploying real-time solutions

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work