~~NOTOC~~

====== Python and Machine Learning resources ======

===== ML open source software and data =====

  * [[http://scikit-learn.org/ | Scikit-learn]].  We will use this library for some of the assignments.
  * [[https://xgboost.ai/ | xgboost]].  Another great tool.
  * The [[http://archive.ics.uci.edu/ml/index.html | UCI]] machine learning data repository.
  * [[http://www.kaggle.com/competitions | ML competitions]].

===== Learning about what's new and exciting in ML =====

  * [[http://www.KDnuggets.com| KDnuggets]] - Big Data, Data Mining, Data Science, and Machine Learning Resources
  * [[http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html | Are we there yet?]]  recent progress on challenging computer vision datasets.
  * 

===== Python =====

=== Resources for learning python ===

  * [[https://docs.python.org/3/tutorial/  | The official Python tutorial]]
  * [[http://greenteapress.com/wp/think-python-2e/|Think Python]], on-line book by Allen Downey

=== Python resources ===

  * [[http://pythonconquerstheuniverse.wordpress.com/category/python-debugger/|Python Debugging Techniques]]
  * [[http://ipython.org/|IPython]]:  a better Python interpreter.
  * If you must use an IDE... [[https://www.jetbrains.com/pycharm/ | Pycharm]].


===== Python on ... =====

==== ... MS Windows ====

  * [[http://docs.python.org/using/windows.html|Using Python on Windows]]

==== ... Macs ====

  * [[http://docs.python.org/using/mac.html|Using Python on Macs]]


===== numpy, matplotlib, scipy, matplotlib =====

  * Numpy [[http://www.scipy.org/Numpy_Example_List|examples]]
  * The Numpy [[http://docs.scipy.org/doc/numpy/reference/|guide]]
  * A [[https://www.labri.fr/perso/nrougier/from-python-to-numpy/| tutorial ]] on how to write efficient code using Numpy.


===== LaTeX =====

Lots of tutorials and quick starts out there:
  * [[http://tobi.oetiker.ch/lshort/lshort.pdf|The Not So Short Introduction to LATEX]] by Tobias Oetiker, Hubert Partl, Irene Hyna and Elisabeth Schlegl, with an excellent index useful for looking up commands
  * [[http://soundandcomplete.com/2010/05/13/emacs-as-the-ultimate-latex-editor/|Emacs as the Ultimate LaTeX Editor]], by Piotr Kaźmierczak
  * [[http://www.maths.adelaide.edu.au/anthony.roberts/LaTeX/ltxqstart.html|LaTeX: from quick and dirty to style and finesse]], by Tony Roberts
  * [[http://www.andy-roberts.net/writing/latex|Getting to Grips with LaTeX]] by Andrew Roberts
  * [[https://www.overleaf.com/ | Overleaf]], [[https://www.authorea.com/ |Authorea]]: Like Google-docs for Latex.

===== Tools for LaTex =====

There are specific editors and GUI programs to help you with LaTex (e.g. texmaker, lyx, and kile for linux, which are installed on CS machines, and miktex for windows).  Personally, I either do things at the commandline and using emacs/aquamacs or use [[https://www.overleaf.com/ | Overleaf]].



===== Remote Access to CS Department Machines =====

See the [[https://www.cs.colostate.edu/wiki/Remote_Access|Remote Access]] page department wiki.



===== Matrices =====

  * [[http://www.johndcook.com/blog/2010/01/19/dont-invert-that-matrix/|Don't Invert that Matrix]], by John Cook
  * [[http://orion.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf|The Matrix Cookbook]], by Kaare Brandt Petersen and Michael Syskind Pedersen

===== Machine Learning Societies, Journals and Conferences =====

  * [[http://machinelearning.org/|The International Machine Learning Society]] and its [[http://machinelearning.org/icml.html|International Conference on Machine Learning]]
  * [[http://jmlr.csail.mit.edu/|Journal of Machine Learning Research]], free, on-line
  *  [[http://www.mitpressjournals.org/loi/neco|Neural Computation]]
  * [[http://nips.cc/|Neural Information Processing Systems]] conference.