Learning and prediction
|
|
CSC423 Learning and prediction |
 |
 |
 |
 |
 |
 |
 |
Course outline:
Topics include: elementary information theory (including noiseless coding and
Huffman codes); elementary foundations of
inductive inference; introduction to Minimum Message Length (MML) inference;
MML approaches to clustering, unsupervised
classification, decision trees, causal modelling, data mining. Applications to
be considered include: image compression,
models of protein folding, bushfire prediction, DNA alignment and the human
genome project, authorship identification for
texts, etc.
Lectures:
Thursdays 3.00 - 5.00pm (S15)
Assessment:
T.B.A.
Back to 2000 Honours subjects