Machine Teaching for Autonomous AI Systems
Learn to design and implement autonomous AI systems using machine teaching principles. Master decision-making strategies and SME-driven AI development.
Learn to design and implement autonomous AI systems using machine teaching principles. Master decision-making strategies and SME-driven AI development.
This comprehensive course introduces the principles of machine teaching for developing autonomous AI systems. Students learn how subject matter experts can effectively train AI to optimize various processes and systems. The curriculum covers the fundamentals of automated vs. autonomous decision-making, machine learning algorithms, and real-world applications. Through practical examples and case studies, participants learn to evaluate use cases, interview SMEs, and design AI solutions that outperform traditional methods. The course emphasizes a practical approach to implementing autonomous AI in industrial and business settings.
4.8
(41 ratings)
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English
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What you'll learn
Select appropriate use cases for autonomous AI implementation
Describe the concept of machine teaching and SME role in AI training
Differentiate between automated and autonomous decision-making systems
Evaluate the pros and cons of leveraging human expertise in AI design
Propose and validate autonomous AI solutions for real-world problems
Develop compelling stories to communicate AI solutions
Skills you'll gain
This course includes:
210 Minutes PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course explores the innovative field of machine teaching for developing autonomous AI systems. Students learn how subject matter experts can train AI to improve and optimize various processes and systems. The curriculum covers essential concepts including automated versus autonomous decision-making, machine learning algorithms, and real-world applications. Through practical examples and case studies, participants learn to evaluate use cases, conduct SME interviews, and design effective AI solutions. Special emphasis is placed on storytelling and communication skills needed to present AI solutions to stakeholders.
An Introduction to Autonomous AI & Machine Teaching
Module 1 · 1 Hours to complete
Analyzing the Problem
Module 2 · 2 Hours to complete
Learning the Solution
Module 3 · 2 Hours to complete
Storytelling
Module 4 · 3 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Leader in Autonomous AI and Machine Teaching
Kence Anderson is the Director of Autonomous AI Adoption for Autonomous Systems at Microsoft, where he has designed over 150 autonomous decision-making AI systems for various commercial applications, including projects with PepsiCo, Bell Flight, and Shell. With a background in mechanical engineering and a passion for teaching inherited from his family, Kence integrates educational principles with technical expertise to create effective AI solutions. His research focuses on how machine teaching can empower operators and engineers to develop intelligent agents capable of solving complex problems.In addition to his role at Microsoft, Kence teaches courses such as "Designing Autonomous AI" and "Machine Teaching for Autonomous AI," sharing his knowledge on the intersection of AI technology and practical application in industry. He is also the founder of Composabl, a platform that enables users to build intelligent autonomous agents without requiring extensive coding skills. Kence's innovative approach to machine teaching emphasizes the importance of human expertise in training AI systems, making significant contributions to the advancement of autonomous technologies across various sectors
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4.8 course rating
41 ratings
Frequently asked questions
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