Explore machine learning applications in quantum simulation through this 46-minute lecture by Cornell University's Eun-Ah Kim at IPAM's Model Reduction in Quantum Mechanics Workshop. Delve into the historical context of quantum mechanics, from X-ray diffraction to projective measurements, before focusing on modern machine learning techniques for quantum emergence. Examine the architecture and training of neural networks, particularly Correlator Convolutional Neural Networks (CCNN), for interpreting image-like quantum data. Discover how unsupervised and supervised machine learning approaches are used to identify new phases in programmable Rydberg quantum simulator snapshots, including edge-ordering and rhombic phases. Gain insights into the application of supervised machine learning for understanding complex quantum correlations and its potential to revolutionize quantum simulation research.