Neural networks component of IML
This part of the IML module will
introduce you to neural networks as a pattern recognition tool. Neural networks
are a biologically-inspired paradigm for computation. That is, the way in
which a result is computed is loosely based on the way in which biological
systems react when presented with stimuli. Typically, neural networks are
applied to classification problems and function approximation problems. Their
defining characteristic is that neural systems "learn" how to perform their
computations, and therefore the solution to a problem need not be explicitly
programmed - it can be derived from data about the problem. There are many
different architectures (structures) for neural networks. The first three lectures
will look at the basic principles and some common architectures for
neural systems. We will then look at how neural networks have been applied to
problems in the bio-informatics area by looking at selected
papers from the literature.
It is not my intention to turn you into experts in neural
computation - that is not possible in the time we have available. However, you
should end up with an appreciation of the way in which neural networks
operate, and of the sorts of problems they have been and could be applied to.
The content of individual lectures may vary from what is given
below. Please listen for announcements in lectures.
refer to main module web page.
Other useful links