Scientific Machine Learning: Introduction to Spin Glass Models

This lecture series introduces the concept of Scientific Machine Learning, focusing on the application of machine learning in various scientific fields. The instructor explains the organization of the course, including flipped classes where students present papers. The lecture delves into the importance of understanding machine learning in scientific research, highlighting the impact of machine learning methods in physics and other sciences. The instructor discusses the connection between machine learning and physics, particularly focusing on mean field spin glass models. The lecture explores the optimal methods for solving clustering problems, Bayesian inference, and the computational complexity of these problems. Additionally, the instructor touches upon the work of Parisi in solving spin glass models and the phase transitions observed in computational problems akin to those in physics.