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

Overview

Explore the principles of confidence estimation in machine learning classifiers, focusing on geometric properties of training data to predict model certainty and manage risk in AI decision-making systems.

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

Subjects

Computer Science