MLMU BA: Deep Learning for Electrical Biosignals – Franz Fürbass
- Language: EN/SK
- Date and time: Wed, January 22th, 19:00
- Venue: ProgressBar, Dunajska 14
Electrical biosignals are commonly used in diagnosis of cardiovascular and neurological diseases like atrial fibrillation or epilepsy. Strict regulatory rules slowed down innovation for a long time but now a large number of startups and industry players like Phillips and Apple enter the market. The talk will focus on deep learning-based methods to analyze Electrocardiogram (ECG) and Electroencephalogram (EEG) and aspects on how such algorithms find their way into FDA/CE cleared devices and software will be discussed.
Dr. Franz Fürbass studied Information and Computer Engineering at the Technical University of Graz (Austria) and received his PhD at the Technical University of Vienna for his work on automatic signal processing of EEG (electroencephalogram). He worked in international companies on solutions for Voice-over-IP and developed systems-on-chip algorithms for cryptography as well as audio signal processing. Since over 10 years he works as scientist at the AIT Austrian Institute of Technology where he is part of the algorithm development team of encevis (www.encevis.com) an innovative medical product for rapid evaluation of EEG. His research interests are applications of AI and deep learning methods to develop solutions in medicine.
Facebook event: https://www.facebook.com/events/539293916674830/
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-deep-learning-for-electrical-biosignals-franz-furbass-tickets-89118625207
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/267783558/
Event: Real (Scary) ML deployment stories
You might expect to see useful (and scary) cases of C-like code in Java (and JVM memory leaks), recompiling libraries just to increase performance two fold,
batching tricks and GPU magic ( on example where each word consists of the different number of subword units and you want to pool those representations).
Vlado might also discuss weird internal workings of matrix multiplication, convolutions and RNNs.Speaker:Vlado Boza has a PhD in bioinformatics from Comenius University. He is very passionate about efficient algorithms and optimization problems. Works at CEAI as ML lead. Currently, he is trying to detect cough from sound recordings. Won some money from Kaggle and is Competition Grandmaster there. Loves Rust. Dislikes Scala.
Facebook event: https://www.facebook.com/events/787616991680127/
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-real-scary-ml-deployment-stories-vlado-boza-tickets-84204414671
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/266814080/
Event: Medical Image Retrieval – Rene Donner
Image retrieval systems in the medical domain support doctors in their decision making, by taking a query image or a region within such a query image as input and finding the most similar images/regions in vast databases. We will be looking at state of the art approches to build such systems, using deep learning for feature extraction and indexing.
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.
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-medical-image-retrieval-rene-donner-tickets-79970246159
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/266187860/
Facebook event: https://www.facebook.com/events/633931594105430/
Event: How to do ML if you have lots of Google’s GPUs
Journey of a student from UK Bratislava into doing research at Google New York as an AI resident. What is AI residency, what is AutoML, what is architecture search, how can one make use of tons of GPUs, why does AdaNet have such an amazing GIF, what does it mean to play at state-of-the-art levels of accuracy in image classification, and what is so far the best indicator for getting into the AI residency?
Vladimir „Vlejd“ Macko is a graduated from UK Bratislava with 4 years of ML startup experience and two internships in Google. He spent his last year in Google research New York as an AI resident. He worked on architecture search in AutoML team AdaNet, and combinatorial optimization for mixed integer program solving in collaboration with DeepMind.
Facebook event: https://www.facebook.com/events/497961537723741/
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-how-to-do-ml-if-you-have-lots-of-googles-gpus-vladimir-macko-tickets-75600253393
Event: Real Estate Price Prediction
The talk will present our efforts over the past year and a half to launch a start-up for US real estate valuation. The start-up called Babcock & Bonbright was built as part of the Central Europe AI portfolio – a start-up studio focused on using artificial intelligence to innovate in the financial and healthcare sectors.
The presentation will focus mainly on the technological aspect of the project – linking structured data, NLP, and image processing to a comprehensive system for estimating the value of dwellings and apartments using machine learning. Attention will also be paid to the processing of map data, system architecture and data pipeline design.
Lukáš Vrábel finished his Master’s in Artificial Intelligence at the Brno University of Technology. There he continued to perform research and education in the area of theoretical computer science. Later he returned to machine learning at Seznam.cz – a local Czech competitor to Google – where he worked at multiple positions ranging from Data Scientist to Head of Research Department. He entered the startup scene by establishing and growing the Brno office for US-EU startup studio Central Europe AI. During his stay there, he also worked on machine-learning based real estate price prediction. Lukáš is currently working as a freelancer, helping various companies with building data science departments, teams, and projects.
Facebook event: https://www.facebook.com/events/597179320792819/?notif_t=event_calendar_create¬if_id=1559587728237461
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-real-estate-price-prediction-lukas-vrabel-tickets-62867381029
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/262011451/?isFirstPublish=true
Pitfalls of Emotion detection in production
Event: Pitfalls of Emotion detection in production
Implementing a deep neural network in production environments presents several challenges, including defining the architecture, training the models, and developing an application. This talk will demonstrate some of the challenges faced, and how you can navigate the terrain of deep learning in real world applications.
Speaker: Justin Shenk is a Data Scientist and Research Engineer with background in neuroscience. His research focuses on identifying signals for deep neural network topology optimization at Osnabrück University with Peltarion.
Statistical vs. Deep Learning Methods for Time Series Forecasting
Who got it wrong?
In recent years, Deep Learning (DL) has revolutionized many fields such as image analysis, speech recognition, and natural language processing. However, Time Series Analysis is still dominated by classical statistical methods. In a recent comparison of statistical (Stat) and machine learning (ML) forecasting methods, prof. Makridakis, one of the leading authorities in this field, even claimed: „The forecasting accuracy of the best ML method was lower than the worst of Stat ones while half the ML methods were less accurate than a random walk“ (Makridakis et al., PLOS One, 2018).In this talk, we start by a high-level introduction to time series forecasting. Next, we get an overview of M1 – M4 competitions‘ results and publicly available datasets on Kaggle. We propose an explanation why for some data, DL forecasting methods are superior, while on the other datasets, they cannot compete with Stat methods. And what can help you to choose in an era of automatically generated time series all around.
Speaker: Petr Simecek recently moved to Brno and joined Central European Ai (CEAi) as a Machine Learning Engineer. Before that, he worked in the US as a Data Scientist for Google and The Jackson Laboratory. Through his career, he went from theoretical concepts (PhD. on structures of conditional independence at MFF UK) through applied statistics (genetic studies on mice at IMG AV CR & JAX) to rather practical Time Series analysis. As a former Software Carpentry instructor, he believes in keeping doors to Data Science wide open, helping others to learn R & Python and looking for more contributors to Daily Python Tip Twitter account.
Travelling salesman problem is NP-hard? No Problem!
Data scientists solve optimization problems every day. Sometimes we are fine with half-assed solutions (e. g. in training of neural networks).
But in this talk we take optimization seriously. I will talk about our experience in two Kaggle competitions (https://www.kaggle.com/c/traveling-santa-problem
), which were related to Travelling salesman problem (TSP). I will talk about main approaches for solving TSP and also how to show that solution is almost optimal. Then we discuss implications of variations encountered at above mentioned Kaggle competitions.
Vlado Boza has a PhD in bioinformatics from Comenius University. He is very passionate about efficient algorithms and optimization problems. Works at CEAI as ML lead. Currently, he is trying to detect cough from sound recordings. Won some money from Kaggle and is Competition Grandmaster there. Loves Rust. Dislikes Scala.
Anomaly detection using time series forecasting models in business
In the beginning we will talk about the theoretical part about time series, forecasting models and evaluations methods. After that, Jan will speak about the process of creating evaluation methodology for the case he was dealing with in Dell. Then he will mention how anomaly detection can be used for marketing purposes in examples such as media monitoring cases, that he was working on in Mediworx.