My Matlab folder for Machine Learning. Contains bunch of different projects. 

Artifical Neural Networks:
mendez - Training data
V1: Self implemented standard artificial neural network, uses batch gradient descent for back prop
V2: Added option for multiple epochs and implemented sequential gradient descent
V3: Added extra layers to play around. 

Bayesian Optimization:
Draw_Gaussians: Simple script that generates data and plots the belonging confidence interval. 
GPOpt: Bayesian Optimization. Use when function evaluations are expensive (Such as hyperparameter optimization). 
Implemented the Expected Improvement algorithm. Kernel is obviously interchangeable, but Gaussian kernel is implemented.

Dimension Reduction:
LDA: Self implemented Linear Discriminant Analysis
PCA: Self implemented Principle Component Analysis
PLS: Self implemented Partial Least Squares

Gaussian Mixture Model:
data_synthesis: Produces .mat file containing two normally distributed datasets. 
GMM: Self implemented Expectation Minimization algorithm that fits K Gaussians.
plotgaussian: Plots the fitted Gaussians. Is required by GMM as it draws every iteration. 

K-Means
K_Means: self implemented algorithm. 
K_Means_ex: Generates data and asks how many clusters you want it to fit. 

Online Support Vector Clustering
OSVC: Main file. Fits a GMM on the data and uses means as data representation. Then iteratively trains a SVC using 
Regularized Follow The Leader with Negative Entry Regularization for Optimal Regret. If the representation is not confident
in a new point, it is saved until memory is full and the GMM representation is updated. Decision boundary is set by a threshold. 

Particle Filter:
Example: Main file. Runs the function file particle_filter. May or may not have copied some code here and there. 

Regression:
Both Linear and Logistic regression. Both with own dataset. 

Support Vector Machine:
Either linear, kernelized or with hyperparameter optimization. 
Comes with seperate prediction file in case of kernelized SVM. 

OSVM is coming. 
Online Support Vector Machine is especially interesting as it's part of a current research project for my graduation project for Tritium.