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Regularization Methods for Machine Learning

Project description

Regularization for Machine Learning

These contents were taugh in summer school RegML 2016 by Lorenzo Rosasco and this GUI in python was submitted as part of final exam.

All the coded and tested functions are in and GUIs code structure is in



Kernal Learning

(Linear, Polynomial, Gaussian)

  • Linear equation1
  • Polynomial equation2
  • Gaussian (RBF) equation3

K-Fold Cross Validation


Regularization for Machine Learning


  3. Getting_Started_Demo.ipynb


Following libraries are required to use all the functions in RegML library

  1. Python(=2.7)
  2. Numpy(>=1.10.4) Numpy
  3. Matplotlib(>=0.98) Matplotlib
  4. Scipy(>=0.12) Optional -(If you need to import .mat data files) Scipy

Tested with following version

GUI is tested on followwing version of libraries

  • Python 2.7 / 3
  • Numpy 1.10.4
  • Matplotlib 1.15.1
  • Scipy 0.17.0

Getting starting with GUI


After lauching python, go to directory containing and files and run following command on python shell

>> run

If you are using Spyder or ipython qt, browes to directory, open file and run it


Open terminal, cd to directory contaning all the files and execute following command

$ python

if you have both python 2 and python 3

$ python2

If you are using Spyder or ipython qt, browes to directory, open file and run it

Getting Started with DEMO

Getting_Started_Demo is a IPython -Notebook, which can be open in Ipython-Notebook or Jupyter


RegML Library

Nikesh Bajaj

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