ml-stanford-solutions

This repository contains all the solutions to the programming exercises of the Machine Learning course offered by Stanford on Coursera, taught by Prof. Andrew NG.

The assignments have been coded in Octave (MATLAB).

Following are the topics I learnt and coded for thise course:

1. Linear Regression - Gradient descent, feature normalization, single and multiple variables
2. Logistic Regression - regularised
3. Multi-class Classification - one vs all classifier training and prediction
4. Neural Networks - Forward and back propagation, sigmoid and regularised gradient
5. Regularised Linear Regression - Polynomial feature mapping, cross-validation curve
6. Support Vector Machines - Gaussian Kernel
7. K-means clustering, PCA - Closest centroid means, principal component analysis
8. Anamoly Detection, Recommender Systems - Collaborative filtering

The assignment descriptions are available in the directory of each assignment.