AI Final Project
Due December 13 (Friday) by noon

Starting point code

Use Teammaker to form your team. You can log in to this site to indicate your partner preference. Once you and your partner have specified each other, a GitHub repository will be created for your team.

Introduction

The goal of the project is to give you several weeks to explore an AI topic of your choice in more depth. In the next section are some suggestions, but feel free to consider other ideas (just be sure to discuss them with me first).

Project Ideas

Here are some potential project ideas:

• Apply one of the local search methods to a new problem
• Apply MiniMax or MCTS to a new game
• Apply approximate Q-learning to a new problem (consider other options in OpenAI gym)
• Apply genetic algorithms to a problem
• Apply deep learning using keras to a new problem
• Tackle a machine learning contest (more on this in the next section)

Machine Learning Contests

In recent years, a large amount of work in machine learning has been motivated by various contests and challenges. One of the earliest and best known was the Netflix prize (official site, Wikipedia), which offered $1M to the team that could improve the site's recommendation system by 10%. The Netflix prize was claimed in 2009; since then machine learning contests have become commonplace.

Find a machine learning challenge of your choice from kaggle. Some of these contests are currently active, with prizes available. Others are inactive, but are still interesting challenges to attempt for a project.

Kaggle competitions vary widely in what sort of data and instructions are provided. You should therefore think carefully about the competition you choose: not just "is it a cool problem?" but also "how hard will this data be to work with?" and "how clearly are the expectations of the competition defined?". Please check with me and describe your plan of attack before you get too involved in a particular contest.

In order to download data, you will need to sign up for a free account. Kaggle also has a discussion forum, which may have useful suggestions, especially if you are working on an active contest.

Project Etiquette

Some of the project ideas will likely involve large data sets that could quickly blow through your disk quota. To avoid this, you can save them to /scratch (instructions), which is unlimited, but isn't backed up.

Also, take a look at the department's suggestions for long running jobs.

Submitting

Before the deadline, you need to submit the following things through git:

• The python code you wrote to implement your experiments
• A README file explaining how to run your code and access your data set(s)
• The latex file with your write-up: project.tex

In addition, you must turn in a printed copy of your project writeup outside my office by the deadline.

In the LaTex file, project.tex, you will describe your project. This file already contains a basic structure that you should follow. Feel free to change the section headings, or insert additional sections. Recall that you use pdflatex to convert the LaTex into a pdf file.

As your project develops and you create more files, be sure to use git to add, commit, and push them. Run: git status to check that all of the necessary files are being tracked in your git repo.

Don't forget to update the README so that I know how to test your code.