 |
Description
The main goals of the class are to (1) obtain an overview of current
state of the art in the field, (2) carry out a substantial research project,
and (3) get practice in research skills such as conducting a literature
study, putting together a research and a conference talk, and writing
a research paper. The course is organized so that selecting and completing
a research project should be as easy as possible. The first part of the
course is an introduction to neural networks. Biological information processing
is first briefly discussed, followed by an overview of the most popular
artificial neural network architectures and algorithms such as perceptrons,
backpropagation, Hopfield and Boltzmann networks, feature maps, adaptive
resonance theory, reinforcement learning, and genetic neuro-evolution.
Distributed representations will be introduced and the foundations of
connectionist artificial intelligence will be discussed. Homework assignments
will give students hands-on experience in building simple network models.
The second part is research oriented. Each student 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 presenter will also select
a paper on that topic for the rest of the class, who will prepare two
questions based on the paper. Based on the specialization, each student
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, optimization, decision making and artificial
intelligence. The project is presented (15 min) in the class, and documented
in a paper.
|
|
