Machine Learning Practical Sessions

This repository gather almost all Machine Learning practical sessions I attended during my studies at the INSA of Rouen & the University of Rouen in 2013 and 2014.

For most of theses sessions, the point was to implement Machine Learning algorithms to get a sense of how they work.

Also, having been written in French schools, most of the comments are in French, although I sometimes wrote them in English. Sorry for non-French speakers. However, code and charts should be understandable.

Organization

Each practical session consist of a folder containing the sources (that you should be able to run) and a report of the results as a PDF file.

Each session is numbered x.y, where x correspond to the number of the course, and y correspond to the number of the practical session of the course. I numbered the courses approximately chronologically.

About the code

The code is either in Matlab (for the most part) or in Python (for a few ones).

Matlab

For each session, the main file that should be run is usually prefixed by an underscore.

For Matlab, the following libraries might be needed:

CVX (very often)
SimpleMKL (very often for monqp.m)
PROPACK (rarely)
PRTools (very rarely)
SOM Toolbox (very rarely)

Python

For Python you will need the scipy environment, pickle, pykalman and yahmm.

The code is available as a Python file or a IPython Notebook.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
1.1 - k-Nearest Neighbors & Cross Validation		1.1 - k-Nearest Neighbors & Cross Validation
1.10 - Mini-project		1.10 - Mini-project
1.2 - k-Nearest Neighbors & Principal component analysis (PCA)		1.2 - k-Nearest Neighbors & Principal component analysis (PCA)
1.3 - Clustering (Hierarchical clustering, K-means, Guassian mixture)		1.3 - Clustering (Hierarchical clustering, K-means, Guassian mixture)
1.4 - Naive Bayes		1.4 - Naive Bayes
1.5 - Gradient descent		1.5 - Gradient descent
1.6 - Optimization without constrain		1.6 - Optimization without constrain
1.7 - Optimization with constrain		1.7 - Optimization with constrain
1.8 - Logistic regression		1.8 - Logistic regression
1.9 - SVM		1.9 - SVM
2.1 - Feedforward Neural Networks		2.1 - Feedforward Neural Networks
2.2 - Self Organizing Map		2.2 - Self Organizing Map
2.3 - Bagging, boosting, classifier ensemble		2.3 - Bagging, boosting, classifier ensemble
3.1 - SVM with kernels		3.1 - SVM with kernels
3.2 - CV for hyperparameters optimization		3.2 - CV for hyperparameters optimization
3.3 - Support Vector Data Description		3.3 - Support Vector Data Description
3.4 - Multiclass SVM		3.4 - Multiclass SVM
3.5 - Multiple Kernel Learning		3.5 - Multiple Kernel Learning
3.6 - Recommendation with matrix factorization		3.6 - Recommendation with matrix factorization
4.1 - Mixture of classifiers		4.1 - Mixture of classifiers
4.2 - Classifier ensemble		4.2 - Classifier ensemble
5.1 - RBF neural network		5.1 - RBF neural network
5.2 - Optimization project (gradient methods, genetic algorithms)		5.2 - Optimization project (gradient methods, genetic algorithms)
6.1 - Wiener-Yule filtering		6.1 - Wiener-Yule filtering
6.2 - Kalman filtering		6.2 - Kalman filtering
6.3 - Hidden Markov Models		6.3 - Hidden Markov Models
6.4 - Extended & Unscented Kalman		6.4 - Extended & Unscented Kalman
6.5 - Particle filter		6.5 - Particle filter
6.6 - Rupture detection (CUSUM, filtered derivative)		6.6 - Rupture detection (CUSUM, filtered derivative)
6.7 - Rupture detection in video (project) @ 4db2a10		6.7 - Rupture detection in video (project) @ 4db2a10
7.1 - Lasso regression		7.1 - Lasso regression
7.2 - Lasso Piecewise Regularization Path		7.2 - Lasso Piecewise Regularization Path
7.3 - Component Wise Lasso		7.3 - Component Wise Lasso
7.4 - Adaptative Lasso		7.4 - Adaptative Lasso
7.5 - Proximal Gradient & Elastic Net		7.5 - Proximal Gradient & Elastic Net
7.6 - Non-Negative Matrix Factoriation & SVD		7.6 - Non-Negative Matrix Factoriation & SVD
7.7 - Proximal Gradient & SVM		7.7 - Proximal Gradient & SVM
7.8 - Non-Negative Matrix Factoriation & Fourier for source separation		7.8 - Non-Negative Matrix Factoriation & Fourier for source separation
7.9 - Stein Unbiased Risk Estimator (SURE)		7.9 - Stein Unbiased Risk Estimator (SURE)
8.1 - Information Retrieval project		8.1 - Information Retrieval project
9.1 - Reccurent Neural Networks & LSTM bibliography @ 70a8863		9.1 - Reccurent Neural Networks & LSTM bibliography @ 70a8863
.gitattributes		.gitattributes
.gitmodules		.gitmodules
1.1 - k-Nearest Neighbors & Cross Validation.pdf		1.1 - k-Nearest Neighbors & Cross Validation.pdf
1.10 - Mini-project.pdf		1.10 - Mini-project.pdf
1.2 - k-Nearest Neighbors & Principal component analysis (PCA).pdf		1.2 - k-Nearest Neighbors & Principal component analysis (PCA).pdf
1.3 - Clustering (Hierarchical clustering, K-means, Guassian mixture).pdf		1.3 - Clustering (Hierarchical clustering, K-means, Guassian mixture).pdf
1.4 - Naive Bayes.pdf		1.4 - Naive Bayes.pdf
1.5 - Gradient descent.pdf		1.5 - Gradient descent.pdf
1.6 - Optimization without constrain.pdf		1.6 - Optimization without constrain.pdf
1.7 - Optimization with constrain.pdf		1.7 - Optimization with constrain.pdf
1.8 - Logistic regression.pdf		1.8 - Logistic regression.pdf
1.9 - SVM.pdf		1.9 - SVM.pdf
2.1 - Feedforward Neural Networks.pdf		2.1 - Feedforward Neural Networks.pdf
2.2 - Self Organizing Map.pdf		2.2 - Self Organizing Map.pdf
2.3 - Bagging, boosting, classifier ensemble.pdf		2.3 - Bagging, boosting, classifier ensemble.pdf
3.1 - SVM with kernels.pdf		3.1 - SVM with kernels.pdf
3.2 - CV for hyperparameters optimization.pdf		3.2 - CV for hyperparameters optimization.pdf
3.3 - Support Vector Data Description.pdf		3.3 - Support Vector Data Description.pdf
3.4 - Multiclass SVM.pdf		3.4 - Multiclass SVM.pdf
3.5 - Multiple Kernel Learning.pdf		3.5 - Multiple Kernel Learning.pdf
3.6 - Recommendation with matrix factorization.pdf		3.6 - Recommendation with matrix factorization.pdf
4.1 - Mixture of classifiers.pdf		4.1 - Mixture of classifiers.pdf
4.2 - Classifier ensemble.pdf		4.2 - Classifier ensemble.pdf
5.1 - RBF neural network.pdf		5.1 - RBF neural network.pdf
5.2 - Optimization project (gradient methods, genetic algorithms).pdf		5.2 - Optimization project (gradient methods, genetic algorithms).pdf
6.1 - Wiener-Yule filtering.pdf		6.1 - Wiener-Yule filtering.pdf
6.2 - Kalman filtering.pdf		6.2 - Kalman filtering.pdf
6.3 - Hidden Markov Models.pdf		6.3 - Hidden Markov Models.pdf
6.4 - Extended & Unscented Kalman.pdf		6.4 - Extended & Unscented Kalman.pdf
6.5 - Particle filter.pdf		6.5 - Particle filter.pdf
6.6 - Rupture detection (CUSUM, filtered derivative).pdf		6.6 - Rupture detection (CUSUM, filtered derivative).pdf
6.7 - Rupture detection in video (project).pdf		6.7 - Rupture detection in video (project).pdf
7.1 - Lasso regression.pdf		7.1 - Lasso regression.pdf
7.2 - Lasso Piecewise Regularization Path.pdf		7.2 - Lasso Piecewise Regularization Path.pdf
7.3 - Component Wise Lasso.pdf		7.3 - Component Wise Lasso.pdf
7.4 - Adaptative Lasso.pdf		7.4 - Adaptative Lasso.pdf
7.5 - Proximal Gradient & Elastic Net.pdf		7.5 - Proximal Gradient & Elastic Net.pdf
7.6 - Non-Negative Matrix Factoriation & SVD.pdf		7.6 - Non-Negative Matrix Factoriation & SVD.pdf
7.7 - Proximal Gradient & SVM.pdf		7.7 - Proximal Gradient & SVM.pdf
7.8 - Non-Negative Matrix Factoriation & Fourier for source separation.pdf		7.8 - Non-Negative Matrix Factoriation & Fourier for source separation.pdf
7.9 - Stein Unbiased Risk Estimator (SURE).pdf		7.9 - Stein Unbiased Risk Estimator (SURE).pdf
8.1 - Information Retrieval project.pdf		8.1 - Information Retrieval project.pdf
9.1 - Reccurent Neural Networks & LSTM bibliography.pdf		9.1 - Reccurent Neural Networks & LSTM bibliography.pdf
README.md		README.md

ThomasRobertFr/MachineLearningPracticalSessions

Folders and files

Latest commit

History