##Introduction

The goal of the data challenge is to learn how to implement machine learning algorithms, gain understanding about them and adapt them to structural data. For this reason, we have chosen a simple image classification task. There are specific **rules** that are described below.

##What is expected
Two days after the deadline of the data challenge, you will have to provide
- a small report on what you did (in pdf format, 11pt, 2 pages A4 max, with your team name and member names written under the title). You will need to upload it on gradescope.
- your source code (zip archive), with a simple script "start" (that may be called from Matlab, Python, R, or Julia) which will reproduce your submission and saves it in Yte.csv

##Rules
- You need to implement **kernel methods** for this challenge.
- You can form teams of at most 2 persons. 
- You can submit results up to twice per day during the challenge.
- A leader board will be available during the challenge, which shows the best results per team, as measured on a subset of the test set. A different part of the test set will be used after the challenge to evaluate the results.
- The most important rule is: **DO IT YOURSELF**. The goal of the data challenge is not get the best recognition rate on this data set at all costs, but instead to learn how to implement things in practice, and gain practical experience with the machine learning techniques involved.

**For this reason, the use of external machine learning libraries is forbidden. For instance, this includes, but is not limited to, libsvm, liblinear, scikit-learn, …**

On the other hand, you are welcome to use general purpose libraries, such as library for linear algebra (e.g., svd, eigenvalue decompositions), optimization libraries (e.g., for solving linear or quadratic programs).