The first part of the course is an introduction to neural networks. We will first review the most important artificial neural network architectures and algorithms such as backpropagation, deep learning, self-organizing maps, reinforcement learning, and neuroevolution. Information processing in biological neural networks will be reviewed and distributed representations will be introduced as a foundation for connectionist artificial intelligence. Understanding of this material will be tested in a closed-book exam. In addition, homework assignments will give students hands-on experience in building simple network models.
The second part is research oriented. Each team of 2 students will select an advanced topic in neural networks, study the literature in depth and give a 50 min presentation to the class on that topic. The presenters will also select a paper on that topic for the rest of the class; each student will prepare two intelligent questions based on the paper.
Based on the specialization, each student (or a team) will then carry out an independent research project. Projects can vary from theoretical analysis of algorithms and development of new mechanisms to applications in engineering and artificial intelligence. The project is presented (in about a 15 min talk) in the class, and documented in a paper.