Introduction to Privacy-Preserving Machine Learning

This course, "Introduction to Privacy-Preserving Machine Learning," delves into the innovative methodologies that enable machine learning models to function effectively while safeguarding sensitive data. Participants will engage in a project-based learning environment that emphasizes hands-on experience with federated learning techniques, allowing them to explore how to train models without compromising user privacy. By the end of the course, attendees will not only gain a solid understanding of privacy-preserving methods but also acquire practical skills that are highly sought after in the job market.
The curriculum is designed to facilitate interactive learning through collaborative projects and discussions. Participants are encouraged to publish their findings in Cademix Magazine, providing them with a platform to showcase their work and contribute to the field. This course is ideal for graduates, job seekers, and business professionals eager to enhance their expertise in AI while addressing the critical aspects of data privacy.
Overview of Privacy-Preserving Machine Learning
Fundamentals of Federated Learning
Data Encryption Techniques for Machine Learning
Differential Privacy in Model Training
Secure Multi-Party Computation
Implementation of Federated Learning Frameworks
Case Studies of Privacy-Preserving Applications
Performance Evaluation Metrics for Privacy-Preserving Models
Hands-on Project: Building a Federated Learning System
Final Project Presentation and Publication Opportunity

Equip participants with the knowledge and skills to implement privacy-preserving techniques in machine learning, enhancing their employability and expertise in the field.

Graduates, job seekers, business professionals, and optionally researchers or consultants interested in AI and data privacy.

Basic understanding of machine learning concepts and programming skills in Python.