It is up to the participants to make sure they have a properly functioning laptop, including a working version of Matlab together with a recent version of PRTools. We would typically stimulate people to work in duos, so if properly coordinated, not everybody needs to take care of the aforementioned [but participant have to take care of this themselves!].
Note also that participants are assumed to have experience with programming in Matlab.
Finally, a minimal working example will be provided a few days prior to the start of the course. With this example, you should be able to check whether the basic functionality of PRTools is present. The prospective participant should make sure that this example works on the laptop that is going to be used in the course.
Access will be provided through guest accounts. Delft University of Technology has the odd policy of making access through eduroam for people from outside of Delft rather complicated.
The course gives an overview of the most important issues in Pattern Recognition. For more background information, the following books are recommended:
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Second Edition, Springer, 2009.
- R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification, Second Edition, John Wiley and Sons, 2001.
- A. Webb, Statistical Pattern Recognition, 2nd edition, Wiley, 2002.
- C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- F. van der Heiden, R.P.W. Duin, D. de Ridder, and D.M.J. Tax,
Classification, Parameter Estimation, State Estimation: An Engineering Approach Using Matlab, Wiley, 2004.
- B.D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996.
Participants may also prepare themselves by reading one or more of the following papers:
- A.K. Jain, R.P.W. Duin, and J. Mao, Statistical Pattern
Recognition: A Review, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 20, no. 1, 2000, 4-37 pdf.
- R.P.W. Duin and E. Pekalska, Open issues in pattern recognition,
in: M. Kurzynski, E. Puchala, M. Wozniak, A. Zolnierek (eds.),
Computer Recognition Systems, Advances in soft computing,
Springer Verlag, Berlin, 2005, 27-42 pdf.
- R.P.W. Duin and E. Pekalska, The Science of Pattern Recognition;
Achievements and Perspectives, in: W. Duch, J. Mandziuk (eds.),
Challenges for Computational Intelligence, Studies in
Computational Intelligence, vol. 63, Springer, 2007, 221-259. pdf
- R.P.W. Duin and E. Pekalska, Pattern Recognition: Introduction and Terminology, 37 steps. pdf
- M. Loog, Supervised Classification: Quite a Brief Overview,
arXiv, preprint arXiv:1710.09230, 2017. pdf
In addition, they should make themselves familiar with Matlab, and preferably with the Pattern Recognition toolbox and other software, that will be used extensively during the course.