Machine-Learning

Machine Learning course from Stanford University

the repo will contain the coding asssignments provided by the course

Exercise 1 - Week 2

Univariate and multivariate Linear Regression using Gradient Descent

Exercise 2 - Week 3

Logistic Regression - the basic and the regularised variants

Exercise 3 - Week 4

Still Logistic Regression, but regularised and also forward propagation for Neural Networks

Exercise 4 - Week 5

Both forward propagation and backpropagation for the same Neural Netowrk as before

Exercise 5 - Week 6

Analysis and visualisation of the Bias - Variance dilemma in Machine Learning

Exercise 6 - Week 7

SVM with both linear and gaussian kernels in order to fit complex decision boundaries with as good a margin as possible

Exercise 7 - Week 8

Unsipervised Learning (K-Means) and PCA; K-Means is used both to cluster point in 2D and 3D space, but also for image compression

Exercise 8 - Week 9

Anomaly Detection by using gaussian distributions and a Recommender System which predicts movie ratings