Education

Teaching

Building AI systems builders, not just AI users.

Courses are structured around working systems, from language models and agents to deployment, monitoring, and responsible use.

22Courses
7Semesters
6Subject areas

Courses

Newest first

2026 Spring

Current

2025 Fall

2025 Spring

2024 Fall

2024 Spring

2023 Fall

2023 Spring

Special

Subject areas

What we make together

NLP & Large Language Models

Transformers · RAG · Prompt Engineering · Fine-tuning · Multimodal · Text Mining

AI Systems & Agents

AIOps · Agent Orchestration · Governance · Responsible AI

MLOps & DevOps

Docker · Kubernetes · CI/CD · Model Deployment · Monitoring

Operating Systems

Linux · Concurrency · Containers · Scheduling · AI Accelerators

Robotics & Physical AI

Collaborative Robots · Automation · Edge AI · Pick & Place

Data Science & Finance

FinTech · Economics · Algorithmic Trading · Network Analysis

Teaching philosophy

Entelecheia

Aristotle's entelecheia, the drive toward full actualization, guides how I design courses. AI education should not produce tool users; it should cultivate systems builders who understand the full stack from model training to production deployment, from data pipelines to responsible governance.

Every course centers on building real systems. Students deploy ML models behind APIs, orchestrate agent workflows, monitor production services, and confront the engineering trade-offs that textbooks abstract away. The goal is that by graduation, they have shipped, not just studied.

In an era where AI agents can write code and automate workflows, the differentiating skill is judgment: knowing what to build, when to intervene, and how to align systems with human values. That is the entelecheia of AI education, technology reaching its purpose through the people who shape it.