Explore the integration of physics into machine learning through this comprehensive 47-minute video lecture. Discover how to incorporate physical knowledge into each of the five stages of the machine learning process: problem formulation, data collection and curation, architecture selection, loss function design, and optimization algorithm implementation. Learn about the importance of physics-informed machine learning in engineering applications, especially for systems governed by physical laws and safety-critical components. Understand how this approach enables more effective learning from sparse and noisy datasets. Dive into case studies, including encoding pendulum movement, and gain insights into physics-informed problem modeling, data curation, architecture design, loss functions, and optimization algorithms. Prepare for an in-depth exploration of AI and machine learning applications in science and engineering.