In conjunction with the University of Washington, Kence Anderson, CEO & Founder of Composabl, and Sarah Cohen, Head of Learning for Composabl, developed the on-demand Coursera course, Machine Teaching for Autonomous AI.

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Learn how to design AI systems that go beyond automation and become truly autonomous. This course covers the fundamentals of machine teaching, where subject matter experts (SMEs) train AI to optimize and enhance decision-making. You'll explore why 87% of machine learning systems fail and how to evaluate whether a problem is suited for an autonomous AI solution.

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What You'll Learn:

• The role of SMEs in training AI
• Differences between automated and autonomous systems
• Strengths and limitations of AI in real-time decision-making
• Selecting effective use cases for autonomous AI
• Designing and validating AI solutions through storytelling

Course Breakdown:

1. Introduction to Autonomous AI – Understand how AI can move beyond automation and hear real-world examples.
2. Analyzing the Problem – Identify when autonomous AI is the right fit over traditional automated systems.
3. Learning the Solution – Explore ML, RL, NN, and DRL techniques, and how machine teaching enhances them.
4. Storytelling for AI – Develop persuasive narratives to justify and fund AI projects.

Through hands-on projects, you'll assess AI opportunities, interview experts, and create a compelling AI design proposal.

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Take the next step in mastering autonomous AI with our individual courses:

Designing Autonomous AI →

Machine Teaching for Autonomous AI →

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