M2-AI Internship Presentations 2024-25

# M2-AI Internship Presentations 2024-25 ## Location **LISN (Belvédère)** Campus Universitaire bât. **507** -- Rue du Belvédère -- 91400 Orsay **Conference Room** ## Program The presentation is scheduled on September 3. We will communicate your exact schedule soon. It should last **strictly between 15 and 20 min**, followed by 10 min of questions from the jury. <iframe class="airtable-embed" src="https://airtable.com/embed/apppG049aAUtlXmUQ/shr3HbWWKy98RRx29?viewControls=on" frameborder="0" onmousewheel="" width="100%" height="533" style="background: transparent; border: 1px solid #ccc;"></iframe> ## To The Students ### Internship Evaluations Your internship supervisor (host institution) must fill in the form on **August 31 (23:59)** at the latest and send a copy of the ***Global assessment* text field** to: Marc Evrard and Thomas Gerald. Here is the link you will have to forward to your internship supervisor: https://airtable.com/apppG049aAUtlXmUQ/shrpHzwWFjddIebrP ### Report Grading Submit the report through [this form](https://airtable.com/apppG049aAUtlXmUQ/shrOvIBfMXpGeFgJs) on **August 31 (23:59)** at the latest.  The internship supervisor and the academic advisor may use the following guidelines to give a grade out of 20 points for the report. * Originality (5 points) Whether the proposed approach, analysis, and/or algorithms contain original ideas. * Scientific and technical quality (10 points) Whether: * The algorithms are implemented clearly and efficiently * Systematic experiments are conducted with comparisons with baseline methods and error bars * Good visualizations are presented * Results are critically assessed * Presentation (5 points) Whether the report is clearly written, well-presented with enough figures and graphs (referenced in the text and with good captions), and a bibliography. ### Oral Presentation Grading The internship supervisor, academic advisor, and jury members may use the following guidelines to give a grade out of 20 points for the oral presentation: * Problem Setting (4 points) Understanding and good presentation of the problem, including a description of the data to be analyzed (if relevant). * State of the art (4 points) Literature review. * Method (4 points) The scientific and technical approach chosen, methodology: relevance and originality. * Results (4 points) Results and critical comparison with other approaches. * Presentation (4 points) Clarity of the presentation and charisma. General information regarding the internship: https://sites.google.com/chalearn.org/ai-master/m2-internships?authuser=0