Introduction to machine learning covering k-means clustering and support vector machines.

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K-Means Clustering

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 Two parts
 Simple Clustering Algorithm
 Using ML with Large Datasets

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 Very elegant
 Scales to large datasets
 It is simple and easy to learn
 Works with unsupervised data

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 Competitive Analysis
 Compare products from Company A with
Company B by clustering them into groups
 Semi-Structured Search Engine
 Show different results to different users
depending on how they are classified
▪ What Google thinks about you:
https://www.google.com/settings/ads/onweb/

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 Multivariate data set
 (i.e. each row is a float[])
 Classification is
labeled
 Not linearly
separable
 Popular for testing
ML Algorithms

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 Iris data in (n-1)! charts

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 E.g. Classifying text documents
 Charting no longer makes sense
 Need to rely derived metrics

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 Euclidian
 Manhattan Distance
 Angle between
 Correlation

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 Many ML algorithms rely on the features
to be in the range of [-1,1] or [0,1]
 K-means will work with any range but for
many distance functions larger ranges will
crowed out smaller ones
 We can use this to emphasize some
factors over others

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 select the number of clusters (K)
 select a seed for each cluster (centroid)
 Do {
 assign each item in the training set to the
closest centroid
 update each centroid to the mean of the
assigned items }
 while (any of the centroids have moved)

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 Number of clusters are known (3)
 Pick seed by randomly selecting 3 rows
from dataset
 We intentionally pick 3 close together for
demonstration

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 Number of clusters
 Distance functions
 Feature scaling
 Datasets
 E.g. included abalone and breast cancer
datasets

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 Faster algorithms
with more data will
often beat slower
algorithms with less
data.

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 Some algorithms do not scale well
 e.g. Layered NN
 can take many days (not suited to tutorials)
 ML algorithms need to be run repeatedly
 Tuning hyper-parameters
 K-fold cross validation
 Feature discovery

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 Random Forest
 Built in, popular and effective
 Leave one out
 My preferred

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 Use a fast algorithm for factor discovery
 Use a slow algorithm for final solution
 Many competitions are won on starting the
slow algorithm as soon as possible