Jul 6, 2013 — Hierarchical Clustering — Hierarchical clustering is a clustering algorithm that aims to create groups of observations or classes based on similar features, x.  It is commonly used for microarray or genetic analysis to find similar patterns of expression, and I’m sure that you’ve seen... Read more

Jul 4, 2013 — Markov Decision Processes — To talk about Markov Decision Processes we venture into Reinforcement Learning, which is a kind of learning that is based on optimization using a reward function.  For example, let’s say that we are training a robot to navigate a space without... Read more

Jul 3, 2013 — Principal Component Analysis (PCA) — What is Principal Component Analysis? **Principal Components Analysis (PCA) **is an algorithm most commonly used for dimensionality reduction that finds a one dimensional subspace that best approximates a dataset.  When you have data with many (possibly correlated) features, PCA finds... Read more

Jul 2, 2013 — Meyer Watershed Segmentation — Imagine that the pixel intensities of an image form a landscape, with lower values (closer to zero, corresponding to black) forming valleys, and higher values (closer to 1, white) forming mountains.  Our image isn’t an image, in fact, it is... Read more