Hybrid Graphical Models and Neural Networks

We first look at a high-level comparison between deep learning and standard machine learning techniques (like graphical models). The empirical goal in deep learning is usually that of classification or feature learning, whereas in graphical models we are often interested in transfer learning and latent variable inference. The main learning algorithm in deep learning is back-propagation whereas in machine learning, this is a major focus of open research with many inference algorithms, some of which we have studied in this course. Deep learning is also often tested and evaluated upon massive datasets.