Python and Machine Learning resources

• Scikit-learn. We will use this library for some of the assignments.
• xgboost. Another great tool.
• The UCI machine learning data repository.
• ML competitions.

• KDnuggets - Big Data, Data Mining, Data Science, and Machine Learning Resources
• Are we there yet? recent progress on challenging computer vision datasets.

• The official Python tutorial
• Think Python, on-line book by Allen Downey

• Python Debugging Techniques
• IPython: a better Python interpreter.
• If you must use an IDE… Pycharm.

• Using Python on Windows

• Using Python on Macs

• Numpy examples
• The Numpy guide
• A tutorial on how to write efficient code using Numpy.

Lots of tutorials and quick starts out there:

• 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
• Emacs as the Ultimate LaTeX Editor, by Piotr Kaźmierczak
• LaTeX: from quick and dirty to style and finesse, by Tony Roberts
• Getting to Grips with LaTeX by Andrew Roberts
• Overleaf, Authorea: Like Google-docs 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 Overleaf.

See the Remote Access page department wiki.

• Don't Invert that Matrix, by John Cook
• The Matrix Cookbook, by Kaare Brandt Petersen and Michael Syskind Pedersen

• The International Machine Learning Society and its International Conference on Machine Learning
• Journal of Machine Learning Research, free, on-line
• Neural Computation
• Neural Information Processing Systems conference.