Mini-project report

The mini project is carried out in groups of 3 to 4 students.

Project overview

The project is to estimate the skill of players, involved in a competition, based on the results of matches between pairs of players. The project follows the whole process of solving a real-world problem using probabilistic machine learning:

1. define a model of the phenomenon of interest,
2. analyze the model to unveil structure,
3. formulate an algorithm to make inferences,
4. use the inferences to create predictions.

Using the techniques taught in the course, you will implement state-of-the-art machine-learning methods based on the Trueskill™ ranking system developed at Microsoft for online matchmaking. You will then apply the methods to rank the teams in the Italian Serie A elite football (soccer) division.

Instructions

All instructions are in the file APML2020-project.pdf Download APML2020-project.pdf

. Please, read it carefully!

 

Dataset

You can download the dataset of results of the Serie A division 2018/2019 here Links to an external site..

Important dates

The deadlines and other important dates for the project are as follows:

Moment	Deadline	For whom	Comments
Release of project instructions	September 1	Teachers
Group registration	September 4	Students
Verify model via quiz	September 11	Students
Report submission	September 29	Students	Both report and contribution statement
Peer-review (of another group's report) assignment	September 30	Teachers
Peer-review submission	October 5	Students
Feedback and grade (pass/revise) from teachers	October 8	Teachers	Both report and peer-review report
Revised peer-review submission	October 14	Students	Only if revision on peer-review is required
Final revised report submission	October 25 (new)	Students	Only if revision on mini-project report is required
Feedback on revised report	October 26 (new)	Teachers

(submission deadlines are one minute before midnight, i.e., 23.59)

Group registration and submission

The group registration and the submission of the report, and the contribution statement are done here in Studium.

Check list before report submission

In order to pass, please ensure that:

• You have read the final version of the report from start to end, and made sure it is readable.
• The report does not contain material copied from elsewhere (all reports are checked for plagiarism using Urkund).
• The report is anonymous (that also goes for the file name).
• The report is written using the NeurIPS style, and is not more than 6 pages long.
• You have written the contribution statement in a separate document which is to be submitted here.

Questions

If you have questions, write in the discussion forum.