Overview
Enhance your knowledge of AI app development for marking math questions, focusing on agent optimization with structured output and evaluation.
Syllabus
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- Course Introduction
-- Overview of Goals and Learning Outcomes
-- Key Tools and Technologies
- Review of Previous Concepts
-- Quick Recap of AI App Development Stages
-- Summary of Previous Optimization Techniques
- Agent Optimization Strategies
-- Advanced Machine Learning Algorithms for Optimization
-- Parameter Tuning Techniques
-- Real-time Optimization Techniques
- Structured Output in AI Models
-- Importance of Structured Outputs for Math Question Evaluation
-- Techniques for Designing Structured Outputs
-- Implementation Challenges and Solutions
- Evaluation of AI Models for Math Marking
-- Metrics for Accuracy and Efficiency
-- Handling Edge Cases in Student Responses
-- Evaluation Frameworks and Continuous Improvement
- Hands-On Project: Optimizing an AI Marker
-- Setting up the Development Environment
-- Implementing and Testing Optimized Algorithms
-- Analyzing and Visualizing Structured Outputs
- Case Studies
-- Successful AI Implementations in Education
-- Common Pitfalls and How to Avoid Them
- Advanced Topics
-- Integrating Feedback Mechanisms into AI Models
-- Ethical Considerations in Automated Grading Systems
- Wrap-Up and Next Steps
-- Summary of Key Learnings
-- Resources for Further Study
-- Preparing for Part 27
- Q&A and Course Feedback Session
-- Open Floor for Questions
-- Gathering Feedback for Course Improvement
Taught by
Tags
On-Demand
