Mini-project report
- Due Oct 17, 2021 by 11:59pm
- Points 1
- Submitting a file upload
- File Types pdf and zip
The mini project is carried out in groups of 3 to 4 students.
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Project overview
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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:
- define a model of the phenomenon of interest,
- analyze the model to unveil structure,
- formulate an algorithm to make inferences,
- 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.
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Instructions
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All instructions are in the file APML2021-project. Download APML2021-project. Please, read it carefully!
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Dataset
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You can download the dataset of results of the Serie A division 2018/2019 here Download here.
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Important dates
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The deadlines and other important dates for the project are as follows:
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Moment Deadline For whom Comments Release of project instructions August 30 Teachers Group registration September 3 Students Verify model via quiz September 10 Students Not mandatory, only recommended deadline to be in "phase" with the project. First report submission October 4 Students Both report and contribution statement Peer-review submission October 7 Students Second report submission October 17 Students Feedback and grade (pass/revise) from teachers October 26 Teachers Both report and peer-review report Revised peer-review submission November 2 Students Only if revision on peer-review is required Final revised report submission November 8 Students Only if revision on mini-project report is required Feedback on revised report November 9 Teachers (submission deadlines are one minute before midnight, i.e., 23.59)
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Group registration and submission
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The group registration and the submission of the report, and the contribution statement are done here in Studium.
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Checklist before first report submission
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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 8 pages long (not including any reference list or possible appendix).
- The pdf of the report is not placed inside the zip-file containing the code.
- You have written the contribution statement in a separate document which is to be submitted here.
Submit the pdf and the zip via the "start assignment" button on the top right of this page (if you don't see the button you are most likely not logged in with your Studium account). Submit the two files in one submission (by adding the second file with "add another file"). The contribution statement should not be submitted together with the report and the code, it should instead be submitted here.
Checklist before second report submission
All items for the first submission applies, except that...
- ... in contrast to the first submission the submission should not be anonymous. That means you should add your names to the author list in the document.
In addition to the items in the first submission
- You have written a few sentences about how you have updated your report based on the peer-review. These sentences are submitted in the comment field when you submit your updated mini-project report.
Questions
If you have questions, write in the discussion forum or ask your question on the helpdesks sessions.
Rubric
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Q1 - Modelling
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Q2 - Conditional independence
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Q3 - Computing with the model
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Q4 - Bayesian Network
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Q5 - A first Gibbs sampler
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Q6 - Assumed Density Filtering
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Q7 - Using the model for predictions
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Q8 - Factor graph
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Q9 - A message-passing algorithm
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Q10 - Your own data
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Q11 - Open-ended project extension
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Disposition
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Language
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Format
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