Seminars run in three groups separately. In this page we list just joint seminars and other activities for broader audience.
13.2.2019, -1.65, 13:45 » PeWe Seminar
- Ivan Srba, Jakub Šimko: Methodological topics (part 2)
- How to write good a method/system proposal
- How to evaluate the contribution of project
- How to write a good abstract, conclusion and other parts of theses
14.11.2018, -1.58, 12:45 » Seminar
- Michal Grňo a Viktor Gregor (Pixel Federation): Games, Data and Marketing – on the way to automation
Vo svete mobilných F2P hier je využívanie používateľských dát neoddeliteľnou súčasťou každodenného rozhodovania. Na prednáške sa najprv pozrieme na niekoľko use case-ov data-driven rozhodovania pri nastavovaní stratégie, ale aj pri samotnom vývoji produktov. Prekvapivo najväčším konzumentom dát je marketingové oddelenie. Na čo sú vlastne marketingu dáta, aké dáta zbierame, na čo sa pozeráme, aké KPIs meriame? Ukážeme si, aký prediktívny model v súčasnosti používame na vyhodnocovanie marketingových kampaní, jeho výhody/nevýhody, validácia, hľadanie nových a lepších alternatív.
12.11.2018, -1.65, 16:00 » Data Science Club
- Dominik Csiba (Innovatrics): Fingerprint recognition: From standard methods to small area matchers
- Viktor Gregor (Pixel Federation): Faster and better A/B tests with Bayesian inference (video)
5.11.2018, -1.65, 16:00 » Data Science Club
- Ján Dolinský (Tangent Works): Automatic Model Building for Time-Series with Application in Energy Industry
- Róbert Magyar (Cellense): Machine Learning in Action – How We Doubled Revenue On A Game With Over A Billion Players
22.10.2018, -1.65, 16:00 » Data Science Club
- Peter Krátky (Instarea): When relational database is not enough…
- Ondrej Brichta (Exponea): Events data processing by Spark
8.10.2018, -1.65, 16:00 » Data Science Club
- Martin Bago (Instarea): How we can quickly find what data we have?
- Jožo Kováč (Exponea): Let’s investigate the Experience
3.10.2018, -1.58, 12:45 » PeWe Seminar
- Jakub Šimko, Ivan Srba: Methodological topics (part 1)
- How to define thesis goals and hypothesis
- How to search and organize literature
1.10.2018, -1.65, 16:00 » Data Science Club
- Dominik Csiba (Innovatrics): From zero to a data science project: corobiapolitici.sk
Dominik is a PhD. graduate from the University of Edinburgh, where he focused on the mathematical optimization behind machine learning. Previously he worked at Amazon as a research scientist intern and at Operam as a data scientist, until he settled at Innovatrics on the position of R&D team leader for Bratislava. Additionally, last year he spent some time as a teacher of Calculus at LEAF Academy, pursuing his passion for teaching. Dominik is a very competitive person with a long list of achievements in both math and chess. His current hobby project is the website corobiapolitici.sk analyzing public data about Slovak politicians.
From zero to a data science project: corobiapolitici.sk
Have you ever wondered how all those data science projects around you looked like at the beginning? What does it take to get tangible results? What technologies do you need to know to even get started? In this talk, we aim to give you some answers to these questions by guiding you through the whole story behind our data science project corobiapolitici.sk. From initial motivations to our current vision, from a single person to a team of people, from individual goals to general usefulness, we offer you the non-idealized version of our adventure. If you ever considered to start your own data science project and you have a lot of questions about how to do so, we believe this talk might give you the answers you are looking for. We hope to see all of you prospective data scientists there!
- Renné Donner (contextflow): Julia – a language for fast numerical code as well as general programming
With a background in electrical engineering René has worked for 8 years at the Medical University Vienna as a researcher in computer vision, focussing on anatomical structure localization and content based image retrieval. He is now CTO at contextflow, applying deep learning to large scale medical image data and developing smart tools to aid radiologists in their challenging tasks.
Julia was designed from the beginning for high performance. It is dynamically-typed, feels like a scripting language, and has good support for interactive use. At the same time it compiles to efficient native code for multiple platforms via LLVM.
In this talk we will look at what Julia looks like, what the ecosystem provides and how to start using it in no time.
19.9.2018, -1.58, 12:45 » PeWe Seminar