Spark sessions 003

When? Feb 25 2025 at 18:00.

Where? Faculty of Computer and Information Science, Večna pot 113, Ljubljana.

What?

Game, Set, Match: Neural Networks in Tennis Video Analysis by Ivan Ivashnev (Senior Computer Vision Engineer, Sportradar) In this presentation, we’ll explore how AI and neural networks revolutionize tennis video analysis. We’ll dive into the models we use, our approach to video processing, and the insights we generate. From identifying player movements to extracting performance metrics, discover how technology is shaping the future of sports analytics.

ML in High Efficiency Production by Luka Androjna ( Cast AI, Senior Data Scientist / ML Guild Master) Going from an experimentation and model validation environment to using models in production is not a trivial task, especially when other constraints come into picture as well, like access to data, limited resources available for inference, latency, deployment method, etc. This talk will give a brief overview of such constraints and explain how they affect our choice in the modelling stage.

Increasing forecast accuracy via statistical inference by Živa Stepančič (Quantitative analyst, GEN-I) We will present the challenge of forecasting electricity demand in Sl energy market and how to strengthen our belief in model predictions. One can increase forecasting probability of energy demand by building a new prediction model, using ensemble models, using domain knowledge or statistical corrections. We are testing the last approach by determining the expected demand at different weather events through modeling prediction bias of weather variables and its effects on energy demand forecast.

TabPFN: Approximating Bayesian Inference with Transformers by Valter Hudovernik (Data Science Student at FRI) TabPFN (Tabular Prior-Data Fitted Network) is a transformer-based foundation model designed for tabular data classification. Trained to approximate Bayesian inference on millions of synthetic datasets, it leverages in-context during inference, enabling fast predictions without retraining. TabPFN outperforms or matches traditional models in accuracy and efficiency on smaller datasets. In this talk, we’ll explore its capabilities and discuss how to integrate it into data science workflows.

Pie Charts: An Apology by Erik Štrumbelj (Researcher at FRI, University of Ljubljana) For as long as I can remember, it has been my mission to point out that pie charts, while engaging, are terrible as statistical plots. To finally put the matter to rest, I studied 100 years of empirical results on pie charts. And while they are indeed very flawed in many ways, they also have some surprising qualities. I’ll share these, along with other insights into visualizing parts of a whole.