Resources

Academic Writing and Research Programming Guidelines

• Notes on Academic Writing for Computer Science (Prof. Zanibbi)
• Research Programming Guidelines (Prof. Zanibbi)
• Writing for Computer Science, 2nd edition, by Justin Zobel. An excellent introduction to writing and the research process in computer science (available in the library)
• The Elements of Style, Fourth Edition by Strunk and White. A short and concise reference on writing clearly and concisely (available in the library).

Books

The books below are available in the library. Our library has many other related sources; try searching the RIT library database using "pattern recognition" as the subject or title. Remember that you may request an inter-library loan if you cannot find what you need online or in the library.

• L. Kuncheva. (2004) Combining Pattern Classifiers, Methods and Algorithms, Wiley.
• van der Heijden, F., Duin, R., de Ridder, D. and Tax, M.J. (2004) Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB.
• R.O. Duda, P.E. Hart and D. Stork. (2001) Pattern Classification (2nd edition), Wiley.
• C. Bishop. (2006) Pattern Recognition and Machine Learning, Springer.
• C. Bishop. (1995) Neural Networks for Pattern Recognition, Oxford University Press.
• S. Theodoridis and K. Koutroumbas. (2009). Pattern Recognition (4th edition), Academic Press.
• K. Fukunaga. (1972) Introduction to Statistical Pattern Recognition, Academic Press.
• P. Devijver and Y. Kittler. (1982) Pattern Recognition: A Statistical Approach, Prentice-Hall.
• K.S. Fu. (1982) Syntactic Pattern Recognition and Applications, Prentice-Hall.
• S. Watanabe. (1985) Pattern Recognition: Human and Mechanical, Wiley.
• R.J. Schalkoff. (1992) Pattern Recognition: Statistical, Structural, and Neural Approaches, Wiley.

Data Sets

Here is a partial list of publicly available data sets.

• MNIST handwritten digit dataset
• The Chars74k dataset (character recognition in natural images)
• LAMP (Univ. Maryland) Document Databases (including Logo, Tobacco-800 document datasets; many tools also available).
• Infty Project Datasets (mathematical notation) ((M. Suzuki et al., Kyushu University, Japan)
• UCI Machine Learning Repository

Python and 'pylab'

• Reference text: Python for Software Design (How to Think Like a Computer Scientist) Allen Downey's excellent introductory text on programming using Python (manuscript is available for free online). Code from the book is available here.
• Tutorials for Python 2.7 (the version of python we're using with the IPython shell ('pylab'))
• Textbook: Python implementations for algorithms shown in the course textbook may be found here.
• python.org - Official Python website.
• Download Releases - Python Releases download site.
• IDLE - Documentation for Python's Integrated DeveLopment Environment (NOTE: python 3 docs)
• matplotlib - the matlab-style plotting library which can be used in stand-alone python programs, or using the interactive IPython shell.
  • These have been installed on the CS computer systems. To use 'pylab' remotely (e.g. on your laptop), you need to open an x-terminal (e.g. using X11 on Mac, using putty or a Cygwin terminal on Windows, or 'terminal' on Linux/Unix systems), and then login using: ssh -Y userid@glados.cs.rit.edu . '-Y' allows new windows to be created on your machine when new plot/figure windows are created by 'pylab.'
  • Quick-start: this provides simple examples of how to use matplotlib in an interactive session with IPython. You should be able to do this using any of the computer science lab machines (e.g. glados.cs.rit.edu) using your CS computer account. If you do not have one, please contact the systems administrators to obtain one. Please make note of the comments on turning interactive plotting on and off (ion(), ioff(), and draw() to force a figure redraw) - as for large data, interactive mode can be slow).