Machine Learning fundamentals

This repository contains the code I produced in the context of the Machine Learning online course taught by Andrew Ng. Each programming exercise involved me to fill in the core functions of each algorithm, the rest of the code belongs to (Andrew Ng - Coursera - Stanford University).

Typical examples of Supervised and Unsupervised learning algorithms have been implemented (see Summary). Each implementation was verified in the context of the course. This repo is therefore a good resource to review the basics of some state of the art machine learning techniques.

Summary

1. Linear regression: predicting best city is best for restaurant-chain expansion
2. Logistic regression: predicting whether a student gets admitted into a university
3. Multi-class Classification using a Regularized Logistic regression: Hand-written digits images and Neural Network(just the forward-propagation)
4. Neural Network Training and the Backpropagation algorithm: Hand-written digits images
5. Machine Learning Problem Solving: handling high bias(underfitting) and high variance(overfitting) problems. (Using Linear and Polynomial Regression)
6. Support Vector Machines: Spam classification (Gaussian kernel) (IMPLEMENTED USING A LIBRARY)
7. K-means (Clustering) and Principal Component Analysis (Dimensionality Reduction)
8. Anomaly Detection: detecting anomalous behaviours in server computers (Gaussian Distribution) and Collaborative filtering: Movie ratings

As suggested by the lecturer, a singular attention was made towards using vectorized implementations instead of traditional for-loops in order to make good use of Linear Algebra library and have models that run faster.

Fitting the parameters of each model

In each exercise, the Batch Gradient Descent algorithm was used to fit the parameters of the model. No Stochastic Gradient Descent(Updating the model's parameters on each example) nor Mini-Batch Gradient descent(Updating the model's parameters on a specified number of examples b) was implemented in this repository.

Memo:

1. Compute the regularization-term for both cost and gradient(to prevent overfitting)
2. Compute the Cost for a given value of Theta
3. Compute the Gradient for a given value of Theta

Running a single application

To run ex1, simply execute ex1 in Octave/MATLAB.

For more details about an exercise (Implementation, Data-set or Context) see the corresponding .pdf.