<commit_before>Title: K-Means Clustering
Date: January 19, 2016 5:30 PM
Location: DOW 2166
Category: Tutorial
Summary: k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.

The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.<commit_msg>testing whether lists work...<commit_after>Title: Clustering the Iris Dataset via K-Means
Date: January 19, 2016 5:30 PM
Location: DOW 2166
Category: Tutorial
Summary: **Interested in Machine Learning?**

**Don't know where to start?**

**Never fear!** In Tutorial 00, we'll discuss what Machine Learning is,
giving a brief survey of its various subfields. Then we'll dive into
a hands-on Machine Learning project, in which we'll:

 0. visualize a dataset of flower measurements,
 1. wonder how to group the data-points into species,
 2. formalize this wonder as a problem in "Clustering",
 3. invent a fully-automatic Clustering algorithm,
 4. implement that algorithm using standard packages, and
 5. analyze algorithm performance.

By the end of our session, you'll have:

 * A Mental map of Machine Learning's lay of the land.
 * Experience using standard packages to analyze real data.
 * Enthusiasm to participate in more of MSAIL's activities!
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