Machine Learning & Deep Learning & Statistics
26 talks from the 2025 edition.
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3 Ways to Speed up Your Regression Modeling in Python ▶ Recording 📝 Transcript
Alexander Fischer
Linear Regression is the workhorse of statistics and data science. Some data scientists even go as far and argue that "linear regression is all you need". In this talk, we will introduce three ways to run regression...
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Accuracy Is Not Enough: Building Trustworthy AI with Conformal Prediction ▶ Recording 📝 Transcript
Chris Aivazidis
Building a good scoring model is just the beginning. In the age of critical AI applications, understanding and quantifying uncertainty is as crucial as achieving high accuracy. This talk highlights conformal...
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Benchmarking Time Series Foundation Models with sktime ▶ Recording 📝 Transcript
Benedikt Heidrich
Recent time series foundation models such as LagLlama, Chronos, Moirai, and TinyTimesMixer promise zero-shot forecasting for arbitrary time series. One central claim of foundation models is their ability to perform...
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Bias Meets Bayes: A Bayesian Perspective on Improving Model Fairness ▶ Recording 📝 Transcript
Vince Nelidov
Bias in machine learning models remains a pressing issue, often disproportionately affecting the most vulnerable groups in society. This talk introduces a Bayesian perspective to effectively tackle these challenges,...
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Blazing-Fast Python in Your Database: Unlocking Data Science at Scale with Exasol ▶ Recording 📝 Transcript
Alexander Stigsen
What if your Python models could run inside your database—at scale, with parallel execution, and zero data movement? Meet Exasol: a high-performance Analytics Engine with native Python support and a massively...
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Conformal Prediction: uncertainty quantification to humanise models ▶ Recording 📝 Transcript
Vincenzo Ventriglia
Quantifying model uncertainties is critical to improve model reliability and make sound decisions. Conformal Prediction is a framework for uncertainty quantification that provides mathematical guarantees of true...
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expectation: A modern take on statistical A/B testing with e-values and martingales
Jako Rostami
This talk introduces a novel Python library for statistical testing using e-values, offering a refreshing alternative to traditional p-values. We'll explore how this approach enables real-time sequential testing,...
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Forecast of Hourly Train Counts on Rail Routes Affected by Construction Work ▶ Recording 📝 Transcript
Sebastian Folz, Dr Maren Westermann
Construction work in national railroad networks often disrupts train traffic, making it vital to estimate hourly train numbers for effective re-routing. Traditionally managed by humans, this process has been...
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From stockouts to happy customers: Proven solutions for time series forecasting in retail ▶ Recording 📝 Transcript
Robert Haase
Time series forecasting in the retail industry is uniquely challenging: Datasets often include stockouts that censor actual demand, promotional events cause irregular demand spikes, new product launches face...
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From Trees to Transformers: Our Journey Towards Deep Learning for Ranking ▶ Recording 📝 Transcript
Theodore Meynard, Mihail Douhaniaris
GetYourGuide, a global marketplace for travel experiences, reached diminishing returns with its XGBoost-based ranking system. We switched to a Deep Learning pipeline in just nine months, maintaining high throughput...
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How to use Data Science Superpowers in real life, a Bayesian perspective
Tim Lenzen
In the data science field, we use all these powerful methods to solve important problems. Most of the time, we do this very well because our data science and machine-learning toolbox fits the problems we tackle quite...
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Interactive end-to-end root-cause analysis with explainable AI in a Python Shiny App
Simone Lederer, Julius Möller
We demonstrate a pure Python solution for exploring and understanding datasets using state-of-the-art machine learning and explainable AI techniques. Our application features a reactive dashboard built with Shiny,...
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Intuitive A/B Test Evaluations for Coders ▶ Recording 📝 Transcript
Thomas Mayer
A/B testing is a critical tool for making data-driven decisions, yet its statistical underpinnings—p-values, confidence intervals, and hypothesis testing—are often challenging for those without a background in...
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Mastering Demand Forecasting: Lessons from Europe's Largest Retailer ▶ Recording 📝 Transcript
Moreno Schlageter, Yovli Duvshani
Ever craved your favorite dish, only to find its key ingredient missing from the store? You're not alone - stock outs can have significant consequences for businesses, resulting in frustrated customers and lost...
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Reinforcement Learning for Finance ▶ Recording 📝 Transcript
Dr. Yves J. Hilpisch
Reinforcement Learning and related algorithms, such as Deep Q-Learning (DQL), have led to major breakthroughs in different fields. DQL, for example, is at the core of the AIs developed by DeepMind that achieved...
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Reinforcement Learning Without a PhD: A Python Developer’s Journey ▶ Recording 📝 Transcript
Jochen Luithardt
Reinforcement Learning (RL) has shown superhuman performance in games and is already delivering value in Big Tech. But despite its potential, RL remains largely inaccessible to most developers. Why? Because...
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Scalable Python and SQL Data Engineering without Migraines ▶ Recording 📝 Transcript
Dirk Jung
This session is for data and ML engineers with a basic understanding of data engineering and Python. It shows how to easily use Python code in Snowflake Notebooks to create data pipelines. By the end, you’ll know how...
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supplyseer: Computational Supply Chain with Python ▶ Recording 📝 Transcript
Jako Rostami
This talk introduces supplyseer, an open-source Python library that brings advanced analytics to Supply Chain and Logistics. By combining time series embedding techniques, stochastic process modeling, and...
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The Forecast Whisperer: Secrets of Model Tuning Revealed ▶ Recording 📝 Transcript
Illia Babounikau
Forecasting can often feel like interpreting vague signals—unclear yet full of potential. In this talk, we’ll cover advanced techniques for tuning forecasting models in professional settings, moving beyond the basics...
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The Foundation Model Revolution for Tabular Data ▶ Recording 📝 Transcript
Noah Hollmann, Frank Hutter
What if we could make the same revolutionary leap for tables that ChatGPT made for text? While foundation models have transformed how we work with text and images, tabular / structured data (spreadsheets and...
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The future of AI training is federated ▶ Recording 📝 Transcript
Chong Shen Ng
Since it’s introduction in 2016, Federated Learning (FL) has become a key paradigm to AI models in scenarios when training data cannot leave its source. This applies in many industrial settings where centralizing...
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Towards Intelligent Monitoring: Detecting Degraded Flame Torch Nozzles ▶ Recording 📝 Transcript
Dominik Falkner
Flame cutting is a method where metals are efficiently cut using precise control of the oxygen jet and consistent mixing of fuel gas. The condition of the nozzle is changing over time: deposits formed during the...
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Transformers for Game Log Data ▶ Recording 📝 Transcript
Fabian Hadiji
The Transformer architecture, originally designed for machine translation, has revolutionized deep learning with applications in natural language processing, computer vision, and time series forecasting. Recently,...
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Unforgettable, that's what you are: Evaluating Machine Unlearning and Forgetting ▶ Recording 📝 Transcript
Katharine Jarmul
Can deep learning/AI models forget? In this talk, you'll explore the realm of machine unlearning, where researchers and practitioners aim to remove memorized examples from machine learning models. This is relevant...
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Unlocking the Predictive Power of Relational Data with Automated Feature Engineering ▶ Recording 📝 Transcript
Alexander Uhlig
Relational data can be a goldmine for classical Machine Learning applications — yet extracting useful features from multiple tables, time windows, and primary-foreign key relationships is notoriously difficult. In...
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What's inside the box? Building a deep learning framework from scratch. ▶ Recording 📝 Transcript
Oleh Kostromin
Explore the inner workings of deep learning frameworks like TensorFlow and PyTorch by building your own in this workshop. We will start with the fundamental automatic differentiation mechanics and proceed to...