The AI Workshop

This is a repository created for a Workshop on Artificial Intelligence and Machine Learning organized by Appsterdam and SciFY. In this repo , there are several files and folders, but first follow these instructions to get setup:

Setup Instructions

1. Install Java, it is REQUIRED! Get it from here
2. Install the Python programming language version 2.7(2.7.6) Get it from here if you are on linux you have python installed. I think that the source code can be interpreted by python 2.6 but i have not tested it
3. Install PyCharm. An Integrated Development Environment for Python by JetBrains. Get it from here download the community edition which is free! OR you can download the eclipse ide and use the PyDev that gives you a python ide!

You can also wite code using your favourite text editor and the python interpreter!

A hands-on introduction to python can be found here

IF YOU HAVE ANY PROBLEM INSTALLING THE TOOLS , REMEMBER: Youtube IS YOUR FRIEND! SO IS THE Google!

Files Contained in this repository

In the Workshop we will build a Naive Bayes Classifier in order to learn how to distinguish good emails from bad emails!

The working directory for you to work on is PythonEmailFilter_HANDS_ON/, in there you will find:

• The dataset/ folder contains files(emails) that we will use as data to learn from.
• The test/ folder contains files(emails) that we will use to test our algorithm.
• The Email.py models an electronic message.
• The exercise_features.py is a two-part exercise to help you see how we can select important features(words) from a collection of data and in the second part we will see how we can transform every example in the collection of data to a vector(a mathematical representation that we will use for statistical processing).
• The FeatureSelection.py contains a class that selects the most important words from our collection(words are ranked using Mutual Information-a real number) and then outputs the first 250 words to a features.txt file.
• The NaiveBayesClassifier.py file is responsible for training the algorithm and it is also used to categorize(classify) an email as good(HAM) or bad(SPAM).When the training is done a file models.txt is outputed. This is what the algorithm has learned.
• The Train.py contains the workflow of the training phase of the whole algorithm(read emails->select features->train algorithm)
• Finally the Filter.py is the spam filter application. It reads the collection of emails to test and classifies every email as HAM or SPAM and outputs the results to a results.txt

There are also the folder PythonEmailFilter_completed/ that contains all the completed source code.

The python_powerpoint/ contains an introduction to python The presentation/ folder contains the theory we will learn and the examples we will do during the workshop