Statistical Machine Learning - Mini project
The mini project is carried out in groups of 3 to 4 students. Please register for a group in the student portal
. The deadline for group registration is on January 28. Should you have any questions regarding the group division, please contact Johan or David.
Project overview
The project is to predict which songs, out of a data set of 200 songs, Andreas Lindholm will like. To your help, you are provided another data set of 750 songs, which Andreas already has labeled with like or dislike. The data consists not of the sound files themselves, but of high level features extracted from them.
Using the methods in the course, you will implement the project in Python (outside the scheduled hours), submit your final prediction to a website, and write a report. You will also peer-review a report from another group, and - if necessary - revise your own report. Outstanding work will be awarded with a gold star, which can raise your final grade.
Instructions
The project instructions will be published according to the date below.
Important dates
The deadlines and other important dates for the project are as follows:
| Moment | Deadline | Comment | |
|---|---|---|---|
| Group registration | January 28 | ||
| Solution submissions | February 20 | ||
| Report submission | February 21 | Both report and contribution statement | |
| Peer-review (of another group's report) submission | February 27 | ||
| Feedback and grade (pass/revise) from teachers | March 6 | Both report and peer-review report | |
| Final revised report submission | March 27  |
Only if revision is required |
(submission deadlines are one minute before midnight, i.e., 23.59)
Check list before report submission
In order to pass (or possibly even achieve a gold star if your report is written such that a thorough understanding of the methods is conveyed and has a technical contribution beyond the minimum requirements), please check that ...
- You have read the final version of the report from start to end, and made sure it is readable.
- Your report is anonymous with the correct title.
- The report is written using the NeurIPS style, and is not more than 6 pages.
- You have written the contribution statement in a separate document.
- You have included everything listed in 4.1 in the instructions.
Good luck!