Explore how machine learning can advance scientific understanding in this comprehensive lecture that examines the intersection of artificial intelligence and scientific research. Learn about the fundamental principles of applying machine learning techniques to scientific problems, with emphasis on developing interpretable models that provide genuine insights rather than just predictive accuracy. Discover methodologies for integrating domain knowledge into machine learning frameworks, understand the challenges of working with scientific data, and examine case studies demonstrating successful applications across various scientific disciplines. Gain insights into the importance of explainable AI in scientific contexts, where understanding the "why" behind predictions is as crucial as the predictions themselves, and explore how machine learning can accelerate scientific discovery while maintaining the rigor and interpretability that scientific research demands.