Machine Learning - Cluster Analysis

Cluster Analysis

I’m running my Machine Learning revision very late and struggling with the second part of the course. If anyone wants to get in touch about any part two stuff I’d be more than happy to chat any time before the exam as it really helps. I shall leave my laptop on overnight so emails (bedforj8@cs.man.ac.uk) and skype messages/calls (Starscent) will wake me up. Here’s what I’ve managed to grasp of cluster analysis.

Machine Learning – Support Vector Machines

Hopefully all of this should now be complete, if not tell me, hehe

Say we have the following graph with set of plotted points:

Machine Learning – Naive Bayes Classifier

Background

There are 3 methods to establish a classifier, these are:

Machine Learning - Decision Trees and Entropy

Decision Trees

If anyone requires further explanation, please get in touch with me (or anyone else for that matter)!

In machine learning, it can be desirable to come up with meaningful if-then rules in order to predict what will occur in the future. For example, "if this and if that then this will probably happen". Decision trees can be built automatically, which can then be used to come up with these if-then rules.

A decision tree is used to investigate huge amounts of data and come up with the most probable outcomes. Each condition is an internal node on the tree. Each outcome is an external node.

Machine Learning – Entropy

Entropy is a probability based measure.

Now, lets have a quick example:

Machine Learning – Decision Trees

I bet you thought that was going to say Decision Boundaries again :D – well… that is… if you’ve read the first 4 Machine Learning posts ;)

Nope, this time is Decision Trees, which are very similar to trees in programming – aka Binary Trees.

Machine Learning – Confusion Matrix

So what happens if our dataset only consists of 5 Cats but 95 Dogs? Well chances are you’ll get a 95% by just predicting everything as a Dog! So this means that you predicted 5 Cats as Dogs.
So what happens when we predict a Cat as a Dog and vice versa? Well chances are we won’t to know what's been predicted as what! This is where a Confusion Matrix comes in! :)

Machine Learning – Training and Testing

Splitting the Dataset

Hmm… Ah… Looking back at the graphs we used for the Cats and Dogs; I've just realised something, they have no units ^_^

Ah well, that’s about to change, haha!

Machine Learning – Artificial Neurons / Perceptrons (Part 2)

So then, time for part 2 of Perceptrons :D

Here im going to give a few examples of the algorithm working, then its onto the next part :)

Machine Learning – Artificial Neurons / Perceptrons (Part 1)

Quick Perceptron Example

Here we go… a Decision Boundary… again. Yes you may now kill me :)

Actually, this time it really isn't that bad, because a Perceptron literally IS a decision boundary. Lets take a look at a rounded decision boundary from KNN:

Machine Learning – More on Decision Boundaries

Okay, so as it turns out i forgot something… again! darn im getting good at this ¬.¬

On the post for K-Nearest Neighbour Part 2, I showed a Graph with a Decision Boundary. Well this is just one type of decision boundary. There are technically more but im going to show 3 of them here.

Machine Learning – K-Nearest Neighbour (Part 2)

Last Time

Last time we learnt about the algorithm that the K-Nearest Neighbour use’s, using Cats and Dogs, if you want more go here!:  Part 1

Machine Learning – K-Nearest Neighbour (Part 1)

Cats and Dogs

Lets take 2 example classes. Say we wanted to be able to recognise the difference between a Cat and a Dog, how would you do this?

Well, firstly we have to identify some features that can easily distinguish them both. Lets have a quick example of a cat and a dog – yeah, seriously :P