Risk Management in AI Models - Confidence Estimation in Machine Learning Classifiers

Overview

Syllabus

• Introduction to Risk Management in AI
Overview of Risk in AI Decision-Making
Importance of Confidence Estimation
• Fundamentals of Machine Learning Classifiers
Types of Classifiers
Decision Boundaries and Margin
• Understanding Confidence Estimation
Definition and Purpose
Common Techniques for Confidence Estimation
• Geometric Properties of Training Data
Geometry in High-Dimensional Spaces
Data Distribution and Its Impact
• Techniques for Confidence Prediction
Probabilistic Methods
Ensembles and Bootstrap Methods
Bayesian Approaches
• Evaluating Classifier Certainty
Metrics and Evaluation Methods
Visualizing Confidence
• Managing Risk in AI Systems
Threshold Setting and Decision Strategies
Balancing Precision and Recall
• Case Studies and Applications
Real-World Examples of Confidence Estimation
Industry Applications and Best Practices
• Ethical Considerations in Risk Management
Bias and Fairness
Transparency in AI Systems
• Tools and Frameworks for Confidence Estimation
Introduction to Software and Libraries
Hands-On Practice with Selected Tools
• Future Trends and Developments
Advances in Confidence Estimation
Emerging Risks in AI Systems
• Final Project and Assessment
Design and Implement a Confidence Estimation Module
Peer Review and Feedback
• Course Summary and Wrap-Up
Recap of Key Concepts
Discussion on Future Learning Paths

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Subjects

Computer Science