Security, Scorpions, AI, Wilds and Coding: The Unexpected - Privacy-Aware Machine Learning and Data Science

Overview

Explore the evolving intersection of privacy-aware machine learning, data science, and security through innovative hacking approaches and emerging AI resilience strategies.

Syllabus

• Introduction to Privacy-Aware Machine Learning
Overview of Privacy and Security in AI
Key Concepts: Confidentiality, Integrity, and Availability
• Data Science Fundamentals
Data Collection and Pre-processing with Privacy Considerations
Statistical Analysis and Its Role in Ensuring Data Privacy
• Privacy-Aware Machine Learning Techniques
Differential Privacy
Federated Learning
Homomorphic Encryption
• Security in Machine Learning
Vulnerabilities and Threat Models
Adversarial Machine Learning
• Innovative Hacking Approaches in AI
Ethical Hacking in Data Science
Case Studies of AI Systems Under Attack
• Building Resilient AI Systems
Robustness and Generalization
Defensive Distillation
• Scorpions and the Wilds: Real-World Applications
Case Studies: Privacy-Aware ML in Healthcare
Case Studies: Security in Financial Services
• Coding Privacy-Aware ML Models
Practical Implementation of Privacy Techniques
Hands-On Exercises: Developing Secure ML Models
• Emerging Trends and Future Directions
AI Governance and Policy Frameworks
Evolving Privacy Regulations and Their Impact on AI
• Conclusion and Ethical Considerations
Balancing Innovation with Ethical Responsibility
The Role of AI Engineers in Shaping Privacy Standards

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Subjects

Data Science