Advanced applications of neural networks (deep learning) 2500-EN-COG-OB2Z-C-2

Neural networks form a very interesting group of computational models with a rich history of applications in cognitive science. Initially they were
devised as a simplified model of biological neurons, but later it was discovered that they may be used to model arbitrary dynamicalprocesses, learn mappings between points in high-dimensional spaces and generalize that knowledge. Research in the field of neural networks was pioneered also by cognitive psychologists, who used them to model processes of attention, language acquisition, language production etc.

In the last few years multi-layer neural networks gained popularity as trainable extractors of meaningful features from unstructured data. Progress in network architectures as well as in computer hardware resulted in unprecedented successes in image and audio recognition, text processing, robotic control. Techniques of transfer learning allow generalization from one domain to another. This makes neural networks not only natural candidates for conceptual models of cognitive processes, but also practical tool for analyzing experimental data.

The course will be structured around concrete applications of neural networks relevant to cognitive science. It should provide students with
intuitions regarding strengths and limitations of these models. After this class students should be able to use existing models and adapt them to
their purposes.