This unit explores techniques for protecting data privacy in machine learning, such as anonymization, differential privacy, and federated learning. Students will learn how to apply these techniques to real-world machine learning projects. Üben mit sofortiger Rückmeldung, kostenlos und ohne Anmeldung.

Weitere Themen

• What is Machine Learning Ethics?
• Ethical Challenges in Machine Learning
• Stakeholders and Responsibilities
• Sources of Bias in ML
• Mitigating Bias in ML
• Evaluating Fairness in ML
• Data Privacy Concerns in ML
• Security Considerations in ML