Provided by

Stanford University

stanford.edu
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coursesFree100hAi Agents Workflow Automation
StudentsAI EngineersResearchers
Explore the cutting-edge field of self-improving AI agents, covering foundational concepts, architectures, and practical applications.

Overview

This advanced course delves into the principles and techniques behind designing and building self-improving AI agents. Students will explore cutting-edge research in agent architectures, learning mechanisms for continuous improvement, reinforcement learning, meta-learning, and multi-agent systems. The curriculum covers foundational theories, practical implementation strategies, and ethical considerations for autonomous AI. Designed for graduate students, AI engineers, and researchers, the course emphasizes hands-on projects to apply concepts to real-world agent development, preparing learners to innovate in the field of intelligent autonomous systems.

Instructor

Chelsea Finn

Assistant Professor of Computer Science at Stanford University

Chelsea Finn is an Assistant Professor of Computer Science at Stanford University, where she leads the IRIS lab, focusing on robot learning and meta-learning.

Learning Outcomes

  • Design and implement architectures for self-improving AI agents
  • Apply advanced machine learning and meta-learning techniques to agent development
  • Evaluate the performance and robustness of autonomous systems
  • Understand theoretical foundations of intelligent autonomy and decision-making
  • Address ethical considerations in the design and deployment of AI agents
  • Develop practical skills through hands-on projects in agent construction