Please
look at the UCI Machine Learning Repository at:
http://archive.ics.uci.edu/ml/datasets.html
This
repository has data from a variety of different problems. Click on a dataset to
get details of the data and links to relevant research papers.
Some
possible projects involving these datasets include:
·
A
comparison of methods (in this case the methods will be straightforward to
implement, typically because of already existing code).
·
A
complete and detailed implementation of a method.
·
Develop
your own solution.
Books:
Introductory
texts:
·
Marsland, Stephen. Machine learning: an algorithmic perspective.
CRC Press, 2011.
· Rogers, Simon, and Mark Girolami. A first course in machine learning. CRC Press, 2011.
More advanced texts:
·
Duda,
Richard O., Peter E. Hart, and David G. Stork. Pattern classification.
John Wiley & Sons, 2012.
·
Trevor.
Hastie, Robert. Tibshirani, and J. Jerome H. Friedman. The elements of statistical
learning. 2nd
edition New York: Springer, 2009. You can download the first edition for free from
http://www-stat.stanford.edu/~tibs/ElemStatLearn/
Find
a dataset for a problem that interests you from the following link:
http://riemenschneider.hayko.at/vision/dataset/
You
will then need to do a search on Google
scholar to find relevant papers
which use that dataset.
Books:
Ponce, Jean, David Forsyth. Computer Vision: A Modern Approach. 2nd edition Prentice Hall, 2011.
The machine learning books listed above are also relevant.
A good
introduction to Space-filling curves from the June 2013 edition of the American
Scientist:
http://www.americanscientist.org/libraries/documents/2013416124139665-2013-05Hayes.pdf
An
example of Space-filling curve use:
http://www2.isye.gatech.edu/~jjb/mow/mow.html
R,
matlab or Java.