Lecture
This lecture explores the application of Boltzmann machines in finance, focusing on generating synthetic data to address challenges like small datasets and data anonymization. The speaker explains the structure and operation of restricted Boltzmann machines, detailing how they learn and generate samples from the same distribution as the training data. The training process, involving stochastic gradient ascent and contrastive divergence, is discussed, along with the conversion of real-valued variables to binary representation. The lecture showcases the effectiveness of Boltzmann machines in learning and replicating the statistical properties of financial data, enabling the generation of synthetic data that closely matches the original dataset's characteristics.