Boltzmann machines are stochastic spin-glass models with an external field used in cognitive science and machine learning. They are named after the Boltzmann distribution and are trained by Hebb's rule. They are part of a more general class of energy based models which use Hamiltonians of spin glasses to define the learning task.

This course provides a comprehensive introduction to deep learning, starting from foundational concepts and moving towards complex topics such as sequence-to-sequence models. Students gain hands-on experience with PyTorch and can fine-tune models through practical assignments. A basic understanding of calculus, linear algebra, and Python programming is required.