Trustworthy Machine Learning

About this course:

This course provides a comprehensive foundation in building trustworthy machine learning systems, with emphasis on generative AI applications. Students will develop both theoretical understanding and practical implementation skills across key areas: model reliability, safety, privacy, fairness, and regulatory compliance. Through extensive hands-on assignments and projects, students will implement solutions using industry-standard tools and frameworks.  This course is ideal for ML Engineers and Data Scientists who need to deploy models in production environments where reliability, fairness, and security are critical, AI Safety Researchers seeking comprehensive understanding of trustworthy AI principles and implementation techniques, Product Managers and Technical Leaders overseeing AI initiatives who must understand regulatory requirements and risk mitigation strategies, Cybersecurity Professionals expanding into AI security domains, Compliance Officers in regulated industries (healthcare, finance, government) who need to ensure AI systems meet regulatory standards, and Graduate Students and Researchers pursuing advanced studies in responsible AI development. The course assumes basic machine learning knowledge and Python programming skills. Industry-First Comprehensive Curriculum This course represents the only program in the industry that provides end-to-end coverage of foundational concepts in trustworthiness, uniquely combining theoretical foundations with hands-on implementation across the full spectrum of ML trustworthiness challenges. While other programs may touch on individual aspects like fairness or privacy, this course integrates privacy-enhancing technologies, security testing, regulatory compliance, generative AI safety, and advanced evaluation methodologies into a single comprehensive curriculum that prepares students for the complete landscape of trustworthy AI deployment in enterprise environments.