Data Science Retreat Demo Day #47

Im Posh Teckel präsentieren Absolventen des Data Science Retreats ihre Abschlussarbeiten. Sieben Projekte zeigen praktische Anwendungen von Künstlicher Intelligenz. Die Themen decken ein breites Spektrum ab. Von Verhaltensprofilen über Ansätze in der Psychiatrie bis hin zu technischen Lösungen in Landwirtschaft und Brandschutz ist alles dabei. Der Abend bietet Raum für Austausch innerhalb der Berliner Daten-Szene. Neben den Projektpräsentationen gibt es Getränke und Pizza.

Hey Berlin Data Folks! We’re looking forward to welcoming you to our next Data Science Demo Day on 20th October. Our Batch 47 participants have been busy working on their final projects, and they’re ready to share what they’ve built. This time, we have 7 projects covering a pretty wide mix of topics, from behavioral profiling and healthcare to career transitions, engineering, agriculture, fire detection, and even fermentation. It’ll be a relaxed autumn evening to see some practical data science and AI applications, meet others from Berlin’s data community, and chat about ideas, projects, and what people are working on. And, of course, we’ll have some pizza and drinks to keep everyone going. Free to Attend! Agenda: 17:30 \- Drinks and Networking 18:00 \- Welcome & Introduction Followed by Project Presentations Project Ideas: 1\. ML\-Based Data Broker Simulator Project by Sascha Alexeyenko How much can your everyday browsing actually reveal about you? This project explores that question by building an ML-based data broker simulator. It combines browsing metadata with website content to look for patterns around a user’s emotions, activities, and behavior, both in the moment and over time. The project also demonstrates how these insights could be used to target people with certain content or recommendations at just the right moment. The idea is to make behavioral profiling feel less abstract and give people a clearer sense of what can be inferred from their browsing data and how those insights might be used to influence their choices. 2\. Personalized Psychiatric Treatment Selection for Depression Project by Aleksandra Garifulina Finding the right antidepressant treatment can be a very individual process. This project explores whether machine learning can help support more personalized treatment selection using patient-level clinical characteristics and treatment-response data. The project looks at supervised...