Machine Learning Foundations and Applications

Catalog description

The ability of recently engineered machine learning algorithms and natural systems to perform inference and generalize well from limited finite data observations poses interesting challenges and open problems. This seminar will discuss both practical algorithms for applications and related rigorous mathematical theory. Examples include the approximation and generative abilities of deep learning with recent types of neural networks, formulations and training of unsupervised methods such as transformers, diffusion-models, autoencoders, and non-neural network approaches such as support vector machines, kernel methods, and probabilistic methods.