Unsupervised Learning & K-means

• Clustering Algorithms, K-means Algorithm
• Centroids
• K-means for non-separated clusters
• Random initialisation
• Elbow method

Dimensionality Reduction

• 2D -> 1D
• Data Compression to speedup training as well as visualizations of complex datasets
• Indexes (e.g. GDP, Human Development Index)
• Principal Component Analysis (PCA), projection
• Data Preprocesing. Scaling, normalization
• [U, S, V] = svd(sigma)
• U = covariance matrix
• Reconstruction from compressed representation

This week is about Support Vector Machine (SVM).

First we will learn about Large Margin Classification, in reference to the larger minimum distance from any of the training samples.

We will study Kernels, and the adaptation to non-linear classifiers.

Choosing landmarks will also be covered.

C parameter will be studied.

Similarity and Gaussian Kernels are also main keywords of this session.

We will get good advice about using SVM vs Logistic Regression vs Neural Networks.