M1-HoML (2025-26)

--- author: Marc Evrard date: 2024 title: "M1-HoML (2025-26)" tags: Edu --- Hands-on Machine Learning ========================= Master 1 Computer Science -- Artificial Intelligence Coordination and lectures: Marc EVRARD Practical works: Yue MA November-December, 2025  MCC = CC × 20% (2 quizzes) + projet × 80% Project ------- ### Information #### Presentation * In Engligh * 15 min presentation + 5 min questions * All group members should participate equaly during the presentation * We recommend using slides for the presentation, rather than showing the raw notebook #### Format to submit * Make your submission (**before your presentation** takes place) here: https://ecampus.paris-saclay.fr/mod/assign/view.php?id=2329188 * Notebook (**only 1** per group): Include code and report (in markdown) * The slides in PDF format (if different from the notebook, **only 1** per group) Don't forget to include **all member names** in the Notebook/Slides. * (Include external Python modules if used) * Do not include data (you should include a link to the data in the NB) * Keep the size of the NB under 10 MB (e.g., avoid using Plotly) #### Report structure 1. Intro (explanation of the task in your own words) 2. Preprocessing (exploration of the data, cleaning, etc.) 3. Training 4. Evaluation 5. Conclusion (what you did; what worked; what didn't; if you had more time...) #### Evaluation of the project presentation (15 min + 5 min questions) * Preprocessing (exploration, cleaning) (/4) * Model (/4) * Code readability (/4) * Performance evaluation and analysis/explanation (/4) * Oral presentation (/4, individual grade) #### Recommendation * Tabular problem (try to avoid NLP or advanced image processing in this class) * The task should not be too trivial (e.g., predicting something easily computed through simple math, or a target too highly correlated to some of the features) * Choose a task with a target hard to obtain from features easier to measure * Size and usability * More than 10 features (ideally more than 100) * More than 1000 instances (ideally from 10k to 1M) * Understandable (explicit) features and classes * SOTA * Most problems you have chosen are already solved (e.g., Kaggle) * Make sure you summarize the results of these solutions in your notebook (or slides) as state-of-the-art * And give arguments for choosing your solution (that must be, of course, original) ### Current schedule <iframe class="airtable-embed" src="https://airtable.com/embed/app0B9xfcNkx0XgfD/shrtiDSbDk3AiM1AL?viewControls=on" frameborder="0" onmousewheel="" width="100%" height="533" style="background: transparent; border: 1px solid #ccc;"></iframe> <iframe class="airtable-embed" src="https://airtable.com/embed/app0B9xfcNkx0XgfD/shrDFc3I7hZ6L43ka" frameborder="0" onmousewheel="" width="100%" height="533" style="background: transparent; border: 1px solid #ccc;"></iframe> <iframe class="airtable-embed" src="https://airtable.com/embed/app0B9xfcNkx0XgfD/shr1sEDUFKeNdZ7lJ" frameborder="0" onmousewheel="" width="100%" height="533" style="background: transparent; border: 1px solid #ccc;"></iframe>

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