Miguel Cabrera - Introduction to SVM using Python

Transcript

1. INTRODUCTION TO Support vector machines USING PYTHON USING THOR‘s HAMMER Miguel Cabrera @mfcabrera http://mfcabrera.com
2. OVERVIEW Tools Intro to Machine Learning Intuition behind SVM Basic usage Some situations Examples
3. TOOLS Every hero needs weapons
6. •  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 :
7. Machine LEARNING
8. Machine Learning Supervised Classification Regression Unsupervised Clustering Feature Learning Reinforcement Learning, Recommender Systems, etc. No reason for Natalie Portman to be
9. Supervised x1 x2
10. UNSUPERVISED x1 x2
11. 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
12. Applications Source: https://speakerdeck.com/ogrisel/trends-in-machine-learning-2
13. Spam Classification
14. TOPIC Classification
15. Sentiment analysis
16. OBJECT CLASSIFICATION
17. Support vector machines
18. 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.
19. WHEN?
20. Source: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html
21. THOR LIKES HIS HAMMER....
22. FOR THE FOLLOWING SECTIONS SEE THE IPYTHON NOTEBOOK FOUND AT: http://bit.ly/1g7VEfG
23. INTUITION BEHIND SVM
24. KERNELS
25. MULTICLASS
26. UNBALANCED DATA
27. TRAINING AND CROSS VALIDATIOn
28. 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.
29. QUESTIONS?
30. THANK YOU