ML_toolbox

ML_toolbox: A Machine learning toolbox containing algorithms for dimensionality reduction, clustering, classification and regression along with examples and tutorials which accompany the Master level course Advanced Machine Learning and Machine Learning Programming taught at EPFL by Prof. Aude Billard.

Go to the ./examples folder to run some simple demos and examples from each method. More in-depth tutorials are provided in tutorials-spring-2016 for testing, parameter optimization, evaluation of the following 4 specific topics.

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Tutorials

For access to the tutorials-spring-2016 contact the current maintainer.

Non-linear Dimensionality Reduction

Topics covered: kernel Principal Component Analysis (kPCA), Laplacian Eigenmaps, Isomaps.

Classification

Topics covered: Support Vector Machine (C-SVM, nu-SVM), Relevance Vector Machine (RVM) and Adaboost

Regression

Topics covered: Support Vector Regression (eps-SVR, nu-SVR), Relevance Vector Regression (RVM), Bayesian Linear Regression (BLR) and Gaussian Process Regression (GPR)

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Examples

Reinforcement Learning

Policy Iteration (PI) and Value Iteration (VI) in 2D grid world, Moutain car example with Temporal Difference (TD) Learning

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3rd Party Software

This toolbox includes 3rd party software for the implementation of a couple of algorithms, namely:

• Matlab Toolbox for Dimensionality Reduction
• LibSVM
• Kernel Methods Toolbox
• SparseBayes Software

You DO NOT need to install these, they are already pre-packaged in this toolbox.

-- The main authors of this toolbox and accompanying tutorials were the TA's from Spring 2016/2017 semesters:
Guillaume de Chambrier, Nadia Figueroa and Denys Lamotte

Current Maintainer: Nadia Figueroa (nadia.figueroafernandez AT epfl dot ch)