Score Matching and Langevin Dynamics for Machine Learning is a method used to learn the score function in machine learning models, which represents the gradient of the log probability density function. This approach allows for tractable training and sampling from the model without requiring access to the true distribution or computing its normalization constant. The method involves minimizing the Fischer divergence between the learned score function and the true distribution, and then using Langevin dynamics to sample from the model. This approach is similar to diffusion models, which also use a Markov chain to gradually convert one distribution into another. By learning the score function, machine learning models can be made more flexible and able to capture nuances in complex datasets.