After successful completion of the course, students:
- Know about current advances in modern neural networks such as deep learning, recurrent neural networks, reinforcement learning and generative modeling.
- Are able to implement advanced neural networks in the Python programming language.
- Understand how neural networks can be used to model cognitive processes.
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A main objective of artificial intelligence is to build machines whose cognitive abilities match (or surpass) those of humans. This is also referred to as artificial intelligence. One way to achieve this goal is by developing cognitive architectures that mimick the algorithms used by our own brains. This success of such an approach relies on a continuous interplay between AI and neuroscience.
In this course, we will explore how modern neural networks provide us with the tools to model cognitive processes in artificial systems and understand cognitive processses in biological organisms.
The course consists of different components:
- During the lectures, students will get acquainted with the theoretical basis and practical development of advanced neural networks. This will be done via presentation and discussion of key papers.
- During the practical sessions, students will learn to implement neural network approaches, related to specific papers discussed in class. To this end, the Pyhton programming language will be used.
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