Projects

Practical work is an essential part of learning. We offer you an opportunity to test your newly acquired machine learning skills against real-life problems. Please, read this page carefully and only then ask questions (in Slack, #projects).

Key dates:

• Team formation (team size is 2 - 4 students), until October 20 (Friday)
• Intermediate presentations November 20 - 22 (5 points out of 25 points)
• Final presentations December 18 - 20 (20 points out of 25 points)

Choosing a project

The first step is to find a project that is aligned with your interests. Below are several options:

From the partners:

Read carefully a list of projects proposed by our partners from this document. When choosing a project from a partner, consider not only a topic and the description of an idea, but also if the data is readily available, if a person is ready to spend enough time with a team, and if the complexity of the project is reasonable and aligns well with your expectations. You take full responsibility of working on this project, so be mindful. Also, when you sign up for a project from the partner make sure that your team does not exceed the limit on the number of teams that they are willing to supervise (this is indicated in the separate field). If you choose a project from one of the partners, we will connect you with the company/person who has proposed a project so that you can get data and some guidance. Note that extra time and care must be invested in communicating with a project owner and meeting their expectations. Project owners will be invited to the final presentations and given an opportunity to influence the final assessment.

From Kaggle.com:

You can do a project by participating in one of the Kaggle.com competitions (e.g. Machine Unlearning or Predicting AI model runtime) or working on some Kaggle dataset (e.g. solar power generation dataset). Kaggle challenges and the datasets are usually supported by ‘kernels’ where people document and publish their analysis code. You must declare all kernels that you use in your project and convince instructors (during presentations) that what you have done is different from the existing solutions and constitutes a sufficient amount of work worth 25 points.

Come up with a project yourself:

If neither option above is of interest to you, feel free to propose your own project, for example, you may decide to analyze some publicly available data (from here, here, here, here or here). Make sure there is a very explicit machine learning component in your project - you can consult with instructors if in doubt. If you already have a team, describe your project and the team in the project document. If you don’t have a team, you can advertise your project in Slack in the #projects channel and try to attract team members.

Team formation (deadline: Oct 20)

After you have chosen a project (or came up with it yourself), you need to assemble a team or join the existing one. You can either advertise your project in the #projects channel or comment on someone else's post, letting them know you want to join. Remember that the team size should be between 2 and 4 people. If someone has already formed a team, get their approval before adding your name to the existing team in the project document.

By the deadline on Oct 20 at 23:59 - Each team must add their project by specifying the title (this is obligatory deadline!), project type (from partners, kaggle, or your own), project description, and team members into the project document. Follow the template in the beginning and check out our example project. If you plan to work on a project that a partner proposed, please, in the project type section specify the project identifier (e.g. based on P01 - Impact of covid 19 on energy consumption in Baltic countries). Also specify which day (Monday, Tuesday, or Wednesday) you want to make your intermediate and final presentation. This would determine which practice session instructors will be grading your presentations.

Intermediate presentation: Nov 20 - 22 (5 points out of 25 points)

Find the order of presentations here: https://docs.google.com/spreadsheets/d/10D0kvdPHTkAGv-OE5VcMXu2Q2VcYmK7r

General guidelines for intermediate presentations:

• Please, do all attend the presentation, if one person is presenting the rest of the team should support in QA. There is a possibility to present online.
• Make sure to fit your presentation into 5 minutes (with slides), if you go over time, we will penalise your team;
• Add your google slides (and only!) to the corresponding google folder: here for Monday, here for Tuesday and here for Wednesday.
• in the presentation make sure to introduce your team and project owner (if applicable);
• briefly describe the problem you are trying to solve (say why it needs to be solved);
• mention progress you have managed so far;
• tell about your blockers/problems;
• Please, specify who in your team is responsible for which part of the work
• lastly, say a few words about future steps (what you will accomplish for the final presentation).

Intermediate presentations are compulsory to proceed to the final presentations!

Final presentation: Dec 18 - 20 (20 points out of 25 points)

• Please, do all attend the presentation, if one person is presenting the rest of the team should support in QA. There is a possibility to present online.
• As intermediate presentations, the final presentation will be held offline (+ over Zoom if necessary) with two practice session leaders grading the presentations.
• You have up to 7 minutes to make the final presentation.
• Add your google slides (and only!) to the corresponding google folder: here for Monday, here for Tuesday and here for Wednesday.
• in the presentation, make sure to introduce your team and project owner (if applicable);
• briefly remind us of the problem you are trying to solve (say why it needs to be solved);
• explain what was your approach to the problem (your methods);
• detail results you have obtained and how they match the initial expectations;
• Please specify who in your team is responsible for which part of the work
• Lastly, describe a few lessons you learned while working on the project.

Add a link to your repository to your presentation. If you have a private repository, give access to the course instructors.

The order of teams and the schedule of intermediate and available here: https://docs.google.com/spreadsheets/d/10D0kvdPHTkAGv-OE5VcMXu2Q2VcYmK7r.

Grading

We will grade your presentations based on roughly the following criteria:

• amount and complexity of work performed (40%),
• quality of presentation (30%),
• degree to which you have completed the initial task (25%),
• being on time (5%).

Project grades are assigned to each team member equally unless there is a good reason to believe that some team members have done significantly less or more work than others. The intermediate presentation gives a maximum of 5 points to every team member, while the final presentation accounts for 20 points. Both intermediate and final presentations are to be done during practice sessions (exceptions could be Monday presentations). Each project will be graded by at least two instructors independently, the final grade will be their average. Getting at least 12 points for the project is a prerequisite for passing the course.

There is no written report for the project. The only thing we will ask you to submit is a link to your project GitHub and presentation (in PDF).

Short summary of what you have to do:

1. Choose a project a) from a partner b) from Kaggle.com or c) come up with a project idea yourself.
2. By October 20, form a team (2 - 4 people including you) using Slack channel #projects or your contacts in the course and describe your project + team here: https://docs.google.com/document/d/10mof4Dw9CN7P2m-kNj-C4PWLkYI_cjwq.
3. Deliver an intermediate presentation on the week of Nov 20 - 22 during a chosen practice session (either on Monday, Tuesday or Wednesday)
4. Deliver a final presentation on Dec 18 - 20 during your chosen practice session