With the growing use of machine learning and generative AI in high-stakes applications, ensuring these models are trustworthy, responsible, and sustainable is critical. This course explores five pillars of trustworthy AI: (i) explainability and interpretability to support human understanding, (ii) robustness against adversarial attacks and real-world uncertainty, (iii) ethical alignment with human values, (iv) privacy-preserving methods for confidentiality and compliance, and (v) sustainability through techniques like model compression and distillation. The course also introduces large language models (LLMs) and emerging approaches to enhance their trust, accountability, and sustainability.