Please look at the UCI Machine Learning Repository at:
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.
· 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:
You will then need to do a search on Google scholar to find relevant papers which use that dataset.
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:
An example of Space-filling curve use:
R, matlab or Java.