This lecture is about clustering. We will introduce the idea of clustering using kmeans as an example methods. We discuss the advantages and caveats of kmeans as well as different ideas behind clustering.
The lecture slides:Lecture 14
The python notebook: cse891 – clustering I.ipynb
the csv test file for weka:test
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This lecture is all about graphs and networks. While this is a very complex topic, this lecture tries to give you an overview and teach core concepts. It should enable you to follow a discussion or talk about a topic that involves graph theory or social network analysis.
The lecture slides:Lecture 13
The exercise:Exercise
The iPhython notebook:cse891 – networks.ipynb
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Please find the “BIG HOMEWORK” here:HW2_AH
And the slides: Lecture 12
the iPython notebook: cse891 categorisation II.ipynb
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This lecture is about regression, please find the slides here:Lecture 11
and the ipython notebook here:cse891 – regression.ipynb
and the exercise here:Exercise 11
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Today we will have the second part of classification.
Here is the ipython notebook:cse891 categorisation II.ipynb
the exercise:Exercise 10
the dataset for the exercise:data
and the slides:Lecture 10
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Now we start talking about data analysis methods, beginning with two sessions about classification.
There are two types of software that helps you do this. The first one is the java program weka, and the second is the python library scikit.learn.
Todays exercise will be about weka, and you can find it here:http://www.cs.waikato.ac.nz/ml/weka/
Scikit.learn which if there is time we will start using today can be found here:http://scikit-learn.org/stable/install.html
The exercise:Exercise 9
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Today is about data visualization.
Please find todays iPython notebook here:cse891 visualization.ipynb
Exercise is this one:Exercise7
the dataset for todays exercise: gestation
Slides can be found here:Lecture 7
