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Munich Datageeks - Introduction to SVM using Python

Munich Datageeks - Introduction to SVM using Python

Accompanying IPython Notebook found at:

Miguel Cabrera

March 25, 2014

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  1. •  Powerful interactive shells (terminal and Qt-based). •  A browser-based

    notebook with support for code, text, mathematical expressions, inline plots and other rich media. •  Support for interactive data visualization and use of GUI toolkits. •  Flexible, embeddable interpreters to load into your own projects. •  Easy to use, high performance tools for parallel computing. From http://ipython.org :
  2. Machine Learning Supervised Classification Regression Unsupervised Clustering Feature Learning Reinforcement

    Learning, Recommender Systems, etc. No reason for Natalie Portman to be
  3. labels labels Supervised Training Tex, Documents, Imgages Training Tex, Documents,

    Imgages Training Tex, Documents, Imgages, Sounds labels Machine Learning Algorithm Predictive Model Features Vectors New Text Document, Images, Sounds Feature Vector Expected Label Adapted from: https://speakerdeck.com/ogrisel/machine-learning-in-python-with-scikit-learn
  4. Support vector machines? •  Effective in high dimensional spaces. • 

    Still effective in cases where number of dimensions is greater than the number of samples. •  Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient. •  Easy to use and train •  Versatile: different kernel functions can be specified for the decision function.
  5. Conclusion •  (I)Python / SciPy / Numpy / Scikit-Learn are

    awesome :D! •  SVM is mature algorithm, straightforward to use and works well in most of the cases. •  Being able to use Kernels allows SVM to learn complex decision boundaries. •  Using LibSVM / LibLinear based libraries allow for reusing models across languages or at least prototyping in Python.