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.

Current version is COMPLETED: 10:47pm - 24/01/10
Please note there have been BIG CHANGES to the 'Virtual Memory' section.

Well here are my notes for Operating Systems, if they help that's great! I'll continue revising them and working on the layout.

Summary completed and updated - 9:06pm - 20/01/10.

I had such a backlog of Computer Networks posts that I've decided to just post the entire thing up. A lot of what's here has been repeated but hopefully bits will be added and issues will be tackled in a different way. I don't know if this will help anyone but here it is and if it does that's great!

I'd like to thank a million people for helping me with networks so far too! :D

Computer Networks - Multimedia

Multimedia

These are my notes... RAR! If there's any issues give me a buzz because some stuff might not be clear enough.

Most multimedia is transmitted via TCP or UDP. It may also be transmitted via RTP and RTCP, but they're just protocols built on UDP - in fact, sometimes they're classified as being part of the application layer. Anyway, let's find out what all of this means...

Financial Reporting - Corporate Governance

Corporate Governance

Let’s start with some more recapping from last time:

• The gearing ratio measures what proportion of a business is funded by borrowing


• Depreciation does not count as a cash expense


• Current assets are usually only held during one operating cycle, so they are expected to be sold within a year

Algorithms - Analysis of Algorithms

Analysis of Algorithms

There are 2 ways of doing this:

• Inspecting pseudo code.  This is the analytical approach


• Implement the algorithm and time it. This is the experimental approach

The analytical approach is usually preferred because:

Algorithms - Complexity

Complexity

Note: You really need to read the algorithms text book chapters 1 to 3 as well as reading these notes!

Second note: This will probably be quite short as I'm cutting out all the exposition from the notes and just including the important information.

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.

Maths / Logic and Modelling – CNF

This is going to be the first post I'm going to write that is valid for BOTH year 1 and year 2. Just in case you get confused as to who should be reading it ;)

CNF is short for Conjunctive Normal Form. A formula is in CNF if it is True (T) or False ( _|_ ) or a conjunction of disjunctions:

Logic and Modelling – Polarity

No, this a not a post about the North and South pole ;)

Logic and Modelling – Splitting

Splitting a formula is not like splitting an atom, haha :)

Splitting a formula is a way of checking the satisfiability of a propositional formula – or a set of formulas. The way we do this is by considering the truth values for Boolean variables occurring in the given formula, and then simplifying the formula depending on these values.
From these simplifications, we may even discover that we don’t always need to find the truth values for all Boolean variables in the formula.

Algorithms – Trees

So what exactly is a Tree in programming?

Well, it is not a tree that you see every day when you walk through a park :P

Financial Reporting - (More) Financial Statement Analysis

More Financial Statement Analysis

Let's start with some recapping from the last section.

• Let's say that a company's average debtor payback time in 2008 was 62 days, and in 2009 was 31 days. A possible reason for this is that the company has improved their credit control procedures, meaning they now collect their debts more quickly.

Financial Reporting - Financial Statement Analysis

Financial Statement Analysis

Who is interested in analysing a financial statement?

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!

Financial Reporting - Cash Flow Statement

Cash Flow Statement

What's the difference between cash and profit? Ok yeah, you know the answer, but could you actually say it in an exam question type way?

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

Computer Networks - An Introductory Overview

What is a computer network?

To a user - something that allows applications to talk to each other. They have an idea as to what the infrastructure should allow them to do. Email and web browsing should be reliable. Internet banking should also be secure. Internet radio, voice over IP and video streaming should have quality. With VoIP however you also want low latency (delay) too, as opposed to internet radio. The demands of the network depend on what type of application you want to run.

Cocoa Framework Summary

A summary by James Bedford.

Cocoa is a framework of tools used for software development written in Objective-C, featuring modular, object-oriented and visual design. It was originally called NextSTEP and was developed by the company NeXT. When NeXT merged with Apple in 1996, the framework NextSTEP was renamed Cocoa. However, it still remains that all objects within this framework begin with the prefix "NS", which helps to distinguished them and prevent accident errors.

This summary is work-in-progress (which will be periodically updated) and may contain mistakes. Please let me know if there are any issues, or if anything needs clarifying.