scipy2013

Talk from SciPy 2013 "A Gentle Introduction to Machine Learning"

Transcript

1. A Gentle Introduction To Machine Learning Kyle Kastner Southwest Research Institute (SwRI) University of Texas - San Antonio (UTSA)
2. Outline • Why Use Machine Learning? • Workflow • Resources • Final Comments http://isl.ce.sharif.edu/
3. Why Use Machine Learning? • Drowning in data • Computers are cheap, humans are expensive • Psychic superpowers (sometimes) http://blog.thepertgroup.com
4. Types of Problems • Regression (Supervised) ◦ Predict housing prices • Classification (Supervised) ◦ Handwritten digit recognition • Clustering (Unsupervised) ◦ Document tagging http://2.bp.blogspot.com
5. Data • from sklearn import datasets • Iris, Digits are excellent for classification • Boston for regression • Any classification dataset (sans labels) for clustering • Very good for generating data http://en.memory-alpha.org
6. Preprocessing • Separate training data • Normalize by subtracting mean and dividing by variance • Use Principle Component Analysis (PCA) to keep structure while reducing dimensions • PCA to plot N-dimensional data in 2D or 3D
7. Selecting an Algorithm http://peekaboo-vision.blogspot.com
8. Linear Regression • Find the "best fit" line • Outliers will greatly affect results • Perform regression into different basis • Basis can be Fourier, polynomial, wavelet, etc.
9. Logistic Regression • Simple method for classification • Uses regression to split classes • Can be very powerful, especially after PCA
10. Support Vector Machine (SVM) • Margin parameter is a configurable "allowed error" to account for class overlap • Boundaries use a semi- arbitrary "kernel" function • Linear, polynomial, wavelet, sigmoid
11. Resources • Scikit-learn documentation and examples ◦ The infamous cheat sheet • Coursera courses ◦ Andrew Ng's Machine Learning • Pattern Recognition and Machine Learning ◦ Christopher M. Bishop
12. Final Comments • Machine learning is a spectrum • Data preprocessing is vital • Prefer simple models to complex ones • Use sklearn
13. Questions? Code on GitHub: https://github.com/kastnerkyle/SciPy2013
14. Bonus: Trends in Machine Learning • Deep networks • Generative models • Unsupervised data from Youtube • Text-to-speech • Image object recognition • Google+ untagged image search