Program – Bratislava

Nadchádzajúce meetupy

Reinforcement learning 1: deep Q networks – Michal Chovanec

  • Language: SK
  • Date and time: Wed, November 4th, 19:00
  • Venue: online, check meetup.com
Sylabus of the talk:
1. Reinforcement learning versus supervised learning

2. Deep Q Network (DQN) – why it’s naive NN not working

3. Advanced architectures: noisy DQN, dueling DQN, rainbow DQN, attention DQN and hands-on practices how to create network architecture

4. Real examples (Atari, Super Mario, Doom 2) – how is network interacting with given world.

Whole talk is full of real examples of pytorch integration and results. Reinforcement learning is a machine learning method, which is trying to find optimal strategy (trajectory) in Markov decision process (in this case, game). Agent (usually deep neural network) is actively interacting with environment, considering current status to choose actions (policy) as output, evaluing rewards and punishments. Goal is to maximize sum of rewards. Agent does not know what is right behaviour, but has knowledge of actions and rewards. Based on this, reinforcement learning is capable of finding better strategy than human.

Speaker:
Michal Chovanec is a researcher in AI field. Currently is working in Tachyum in Bratislava, developing AI accelerator of new processor. His PhD degree is from University in Žilina. His favorite topics are reinforcement learning, robotics (winner of Gold Medal in Istrobot challenge) and modelling of red blood cell in research group Cell in fluid (FRI, UNIZA). His hobbies are hiking, archery and martial arts.

 
Facebook event: https://www.facebook.com/events/774812223299284/
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/274079064/

Minulé meetupy

Early Stage ML Startup
Vol.1: Research Lessons Learned – Adam Kolář

  • Language: EN
  • Date and time: Wed, February 19th, 19:00
  • Venue: ProgressBar, Dunajska 14
Abstract:
Jumping to the startup world more than 2 years ago was for me something totally new, challenging and honestly a bit scary in the beginning.

During those 2 years of building the company from scratch, we experienced many ups and downs at SimpleFinance, from pivoting the product vision, changing completely the codebase with new data providers to crafting the state of the art ML engine in property valuation used for speeding up and refining mortgage experience on US market.

It was a rush with many lessons learned. So it’s time to share. In this talk, I’d like to cherry-pick a few interesting problems we faced with our data, I will mention ideas over the modelling approach, hyperparameter optimization, and confidence evaluation, discuss a few examples of how tight research setup and production should be and also give you a few tips on how to balance fast research exploration with stable continuous development…

Speaker:
Adam is a chief data scientist at SimpleFinance – Silicon Valley based startup revolutionizing mortgage industry using machine learning. In the past, Adam worked as researched at Seznam, improving the relevancy of web search as well as transforming image search to modern deep learning version. Adam has also experience from GoodAI and the work on AGI, he established Brno ML meetups back in 2017 and nowadays prepare lectures for people from industry about image processing with neural networks via MLCollege project.

 
Facebook event: https://www.facebook.com/events/174975030443479/
Eventbrite ticket: https://www.eventbrite.com/e/mlmu-ba-early-stage-ml-startup-vol1-research-lessons-learned-adam-kolar-tickets-92493457421
Meetup.com event: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/268382607/

MLMU BA: Deep Learning for Electrical Biosignals – Franz Fürbass

  • Language: EN/SK
  • Date and time: Wed, January 22th, 19:00
  • Venue: ProgressBar, Dunajska 14

Event:

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.

Speaker:

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).
Speaker:
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.

Speaker:

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?

Speaker:

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
Meetup.com: https://www.meetup.com/Machine-Learning-Bratislava-Meetups/events/265457334/

 

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.

Speaker:

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, https://www.kaggle.com/c/traveling-santa-2018-prime-paths), 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.

Jan Deak has a Statistical degree from University of Economics in Bratislava and works as a Data Scientist in Mediworx. He has more than 4 years of experiences working as Data Scientist (GFK, Dell, Mediworx). Mostly focused on text mining, AI, anomaly detection, CRM data, forecasting and classification problems.

 

How AI helps robots to see and manipulate objects (Michal Malý)

Humans are perfect at object handling. For a very long time, industrial robots were doing only simple, hard-coded manipulation. High-quality 3D scanning enabled us to localize objects precisely, and plan their picking and movement to avoid collisions. Localization and picking are tasks that we are currently solving with both analytical and ML approach. We compare the advantages and disadvantages of the approaches.

Michal (34) received his PhD in the field of Computer Science, Artificial Intelligence at FMFI UK in 2013. He is an expert in construction of rational agents based on reinforcement learning. He also worked in the area of computer security and published an innovative solution of secure distributed computing. He is currently Director of AI in Photoneo. His vision is to improve computer understanding of 3D environment and enhancing comprehension of real-world objects.

 

Towards Interfaces in Natural Language (Zuzana Nevěřilová)

Smart search as an essential part of the Konica Minolta Cognitive Hub needs to “understand” search queries in English. Being very different from what we use for searching the Web, smart search uses natural language processing and machine learning methods to detect the user’s intent. Afterwards, it extracts all the information the user mentions and creates relationships with the internal knowledge base. The talk will present methods Konica Minolta uses in the natural language query parser and discuss the challenges in creating natural language interfaces.

Zuzana Nevěřilová finished her PhD at Masaryk University in Brno in 2014. She works as a researcher at the Natural Language Processing Centre at Masaryk University where she focuses on knowledge bases and semantics. From June 2018, she is member of the Konica Minolta Laboratories Europe.

 

 

AI/ML for Scalable Recommender Systems (Jakub Mačina)

Recommender systems are successfully used in several domains such as e-commerce or multimedia recommendation. In the e-commerce field, increasing number of companies utilize recommender systems to offer a personalized shopping experience for their customers.

In this talk, we will provide an overview of typical recommender system architecture, approaches used in the real-world applications and we highlight the challenges encountered by industry practitioners. We will discuss basic approaches, such as matrix factorization and text mining, and move on to deep learning techniques for recommendation. In the real-world case study we will show how to capture attributes of products into a dense (latent) embedding representation. Finally we will focus on evaluation metrics and interactive learning for optimizing long-term revenue of retailers. Everybody with an interest in machine learning is welcomed.

Jakub Mačina is the Recommendations Team Lead at Exponea. He graduated from the Slovak University of Technology with specialization in machine learning and information retrieval. His research background is in the field of recommender systems with a publication at the premier conference on recommender systems research ACM RecSys. He is passionate about movement and open source software.

Exponea Experience Cloud is an award-winning customer experience and data management platform that boosts e-commerce growth with AI-powered engagement automation and helps improve company culture with improved cross-department collaboration and customer centricity.

 

 

Machine Learning Challenges in DNA Sequencing (Tomáš Vinař)

MinION is a pocket-sized device that plugs into a USB port of your computer and can decipher your DNA. The device measures changes in electric current as DNA molecules pass through nanopores, resulting in sequences of current measurements called squiggles. It turns out, that interpreting these squiggles is neither easy nor fast. In fact, machine learning methods are the key to bringing this device to Africa to monitor most recent disease outbreaks, or to the desk of your medical practitioner to determine personalized medical treatments.

Tomáš Vinař has studied PhD at the University of Waterloo in Canada, after which he has spent some time at Cornell University in USA as a postdoc. Currently, he is an associate professor and one of the principal investigators of the Computational Biology Research Group at the Faculty of Mathematics, Physics and Informatics of Comenius University in Bratislava. Besides doing bioinformatics research in collaboration with biologists, he teaches bioinformatics, machine learning, and algorithms.

 

 

Can deep neural networks be used on embedded devices? (Marián Beszédeš)

Usage of deep neural networks is often connected to powerful (multi)GPU machines. However, we want to use their potential on every piece of hardware, even on mobile phones. It is still not straightforward to use them on embedded devices because of their size, resource consumption, performance and the like.

Nevertheless, there are techniques which can be used to make deep neural networks and embedded devices more compatible. We will talk about specialized neural networks architectures, quantization, mobile platform optimized NN libraries and even more.

Marián Beszédeš is a computer vision team leader at Innovatrics. His team works on problems related to face processing (detection, tracking, recognition, etc.) on various platforms ranging from GPU servers to ARM devices. He received his Ph.D. from FEI STU Bratislava.

 

Using object detection with CNTK to classify hotel images (Karol Żak)

In our next talk you can look forward to a presentation that will reveal ML application in software products – our speaker Karol Żak will show you how they used object detection with FasterRCNN using CNTK to classify hotel images. All the learnings they share, come from cooperation with customer on real-life solution – platform Hotailor with nearly a million hotels inventory using smart analytics to help businesses sell more hotel rooms.

Karol Żak is Technical Evangelist @Microsoft – a technology enthusiast for years associated with Microsoft and IT communities in Poland. He is also former Microsoft Student Partner, founder and ex-leader of .NET Group in Technical University of Radom. Karol is all about new technologies and trends in IT but mostly interested in cloud computing, AI/ML and mobile tech.
For 2 years as a certified trainer with MCT title, he led IT trainings for developers and then in 2014 joined Microsoft as a Technical Evangelist. Privately he is a big american football fan and starting quarterback for one of Warsaws teams and also Polish National Team.

 

 

Implications of adversarial environment on machine learning (Michal Nánási)

Spam is an adversarial problem. Adversaries at the other end of the internet connection are trying to get around your detection. Each time you deploy an anti-spam measure, adversaries are no longer making money. If your service is big enough they are motivated to go around your detection every time you improve it. More sophisticated adversaries can make your classifier worthless in less than 15 minutes. That is one of the reasons why you still see spam today.

 

In this talk, the we will talk about the problems you may encounter if you try to apply machine learning in adversarial environment; when an adversary is constantly trying to circumvent your detection. Talk will be focused less on specific models, and more on how this environment affects your workflow and the way you approach machine learning problems.

 

After finishing his PhD in Bioinformatics (FMFI UK, Bratislava), Michal Nánási worked more than 3 years at large tech company (over 1 billion of registered users) as software engineer, mostly working on protecting users from high-volume abuse with, and without machine learning.

 

Speech data mining: Not Yet Ready for Retirement (Honza Černocký)

Speech data mining is an important discipline of machine learning. In addition to classical ML and computer science components, it also sources other sciences such as physiology, lexicography, and phonetics, making it a funny inter-disciplinary domain. Similarly to other ML sub-fields, it has been turned upside-down in the recent years by the massive use of neural networks. Although their use in speech dates back to 2000, it is only around 2010 they took the ground and started to dominate the field.

 

Brno speech group has been through all these changes, sometimes following the others, sometimes defining the history. The talk will cover these developments as well as some new research trends.

 

Honza Černocký is Associate Professor (Docent) and Head of the Department of Computer Graphics and Multimedia at the Faculty of Information Technology, Brno University of Technology (FIT BUT). He also serves as the managing director of BUT Speech@FIT research group. He was with ESIEE Paris, France and OGI Portland, Oregon, USA. His research interests include signal processing and speech data mining (speech, speaker and language recognition, keyword spotting and spoken term detection). He was PI and co-PI of several local, European and US-funded projects. He served as co-chair of IEEE ICASSP 2011 in Prague and co-organized IEEE ASRU 2013 in Olomouc. As faculty member of FIT BUT, he teaches signal-processing, pattern recognition, and speech related courses. Honza co-founded Phonexia in 2006 and helped launching two other speech start-ups.

 

Time series prediction with Neural networks: Applications, Pitfalls and Beyond (Rudradeb Mitra)

One of the key application of AI and IoT is making time-series predictions. Companies are using predictive analytics in various ways – from predicting customer buying behavior to predicting health risk to predicting potential future accidents.

 

In this talk, the speaker will explain using three cases how are companies combining time series data and Neural networks to do predictions. Then he will explain what are the pitfalls in the approach of using Neural networks for predictions and what other kinds of algorithms are being or can be developed to overcome the hurdles.

 

Rudradeb Mitra worked in 4 European AI groups and had published 10 research papers on various AI topics including language processing, semantic web, 5th generation languages, and multi-agent planning. After finishing his Masters from Univ. of Cambridge he went on to build 4 startups – two in Silicon Valley, one in UK and one in Belgium. One of his startups was working on predicting potential future car accidents. These days he spends most of his time speaking and writing on topics related to Artificial Intelligence, startups and sales.

 

ML Engineer’s look at insurance (Peter Zvirinský)

Insurance is one of the oldest applications of mathematical modeling. The first statistical models designed specifically for insurance date back to the 18th century. Ever since has insurance modeling been often considered a separate topic from general mathematics/statistics and it is usually taught as a separate field of study on most universities.

 

This talk will present an ML Engineer’s look at insurance and you will find out what makes insurance so different from other typical ML applications. In the course of this talk all components of a typical insurance model will be broken down and you will be shown methods which can be applied for modeling each component. You will also find out what a tail risk is and why it is so important to insurance companies. Lastly, you will be shown why sometimes using a traditional statistical approach might still be prefered to a modern ML approach, and how one wrong assumption regarding your data can lead you to completely erroneous conclusions.

 

Peter Zvirinský is a Machine Learning Engineer at CEAI, where he works on TowerStreet, which is CEAI’s cyber insurance venture. He is also pursuing his PhD in Theoretical Computer Science on Faculty of Mathematics and Physics, Charles university. Prior to CEAI, Peter worked as an ML researcher at Seznam.cz’s online advertisement department.

 

Cesta vidiaca autá, ktoré po nej jazdia (Adam Herout)

„Lidi musí od rána do večera chodit do práce, aby si mohli koupit auto, které jim slouží k tomu, aby mohli jezdit do práce,“ řekl mi kdysi kamarád a možná na tom něco je. Doprava po silnici je tu s námi a je zdrojem radosti, úspěchu i mnohého trápení. Nemám recept, jak se toho trápení zbavit, vím ale, jak v tom trochu pomoct – zjišťovat o dopravě informace pomocí kamer. V přednášce promluvím o tom, v čem je potíž a jak s ní bojovat – pomocí strojového učení, geometrie a práce s daty.

 

Adam Herout – profesor na Fakulte informačných technológií VUT v Brne, kouč, „Někdy jen tak sedím a přemýšlím. A někdy jen tak sedím.“

 

Adam Herout je profesorom a vedúcim skupiny Graph@FIT na Fakulte informačných technológií VUT v Brne, kde se zaoberá počítačovým videním a rozšírenou realitou. Mimo akademickej činnosti je Adam Herout notorický zakladateľ a kibic startupov, ktoré sa točia okolo computer vision a machine learningu.
Okrem akadémie a biznisu Adam koučuje, a to hlavne profesionálov z oblasti IT.

 

3D Reconstruction in Wild (Martin Bujnak)

Photogrammetry is a seemingly magical process that can turn a set of photos into a detailed 3D model with only a PC. While not far from the truth, like most magic tricks, the details make the difference. In this talk Martin will cover how a pipeline for photogrammetry makes the difference between a poor reconstruction and an amazing one.

 

The talk will detail the technology behind and use of a specific photogrammetry tool, Reality Capture, and the use of that tool’s new SDK to extend the pipeline for better results. It will show how using an SDK to add instant, on-site, visual feedback during capture can make the process more reliable and successful.
While it might look magical, there are still many opened questions and opened problems to solve.

 

Martin Bujnak co-founded Capturing Reality which revolutionized process of 3D model creation from photos and laser scans.
He received his Ph.D. in computer vision from Center for Machine Perception in 2012 and the RNDr. and M.S. degrees in computer graphics and parallel computation from Comenius university in 2005. He pioneered and published several minimal solvers for camera pose estimation on major computer vision conferences and journals. He gained development skills in Caligary, Micrsoft live labs and Xbox.

 

Machine learning in adversarial environments for malware detection (Jakub Debski)

Recent discoveries in the field of neural networks and cheap parallel processing have enabled machine learning to be applied successfully to many data processing tasks. Its success has been so impressive that Machine Learning became a buzzword, and now every company tries to jump onto a “machine learning bandwagon” in order to be perceived as a “high-tech”. Unfortunately, too much marketing leads to too much gibberish.

 

This talk addresses recent claims from startup vendors and presents reasons why Machine Learning is not a silver bullet for malware detection. In IT security we have to contend with evasion techniques that are being developed in parallel to detection methods, and machine learning is no exception. The talk covers recent observations and results in this field and looks at evasion techniques from a security company’s perspective. It also explains why human oversight is still crucial in the classification process and why in adversarial environment a signature-less solutions face the same issues as the “old style anti-virus”.

 

Jakub Dębski, living currently in Bratislava, originally from Poland. Almost 20 years of professional experience in IT security out of which the last 10 years in ESET, currently as Head of Core Technology Development. Did master thesis in using neural networks for malware detection in Military University of Technologies / Warsaw. Since then actively tracking progress of machine learning and introduced the topic to ESET for classification of malware.

 

Seznam.cz – Vyhľadávanie obrázkov z (nielen) českého internetu (Lukáš Vrábel)

Lukáš Vrábel so Seznamu.cz nám priblíži, ako funguje Vyhľadávanie obrázkov z (nielen) českého internetu. Môžete sa tešiť na prelet históriou obrázkového vyhľadávania na Sezname – od jednoduchých textových prístupov až po hlboké neurónové siete.

 

Lukáš má v Sezname na starosti menší výskumný tím, ktorý pracuje na realizácii rozličných úloh strojového učenia od analýzy textu a webových stránok až po rozpoznávanie obrazu, takže sa máme na čo tešiť!

 

Využitie machine learningu v tvárovej detekcii a verifikácii (Marián Beszédeš)

Marián bude rozprávať o tom, kde všade pri spracovaní obrazu tvárí môžeme použiť rozličné techniky ML a aký bol ich vývoj. Zameria sa na techniky súvisiace s (hlbokými) konvolučnými sieťami aplikovanými na problémy tvárovej detekcie a verifikácie. Súčasne uvedie aj skúsenosti s ich optimalizáciou a reálnym nasadením (ladenie rýchlosti, vývojové postupy, použitý SW).

 

Marián Beszédeš je computer vision team leader v Innovatricse, kde sa zaoberajú problémami detekcie, trackingu, segmentácie a rozpoznávania tvárí. Touto problematikou sa za zaoberal už v rámci inžinierskeho a doktorandského štúdia na FEI STU. V minulosti sa zaoberal aj BIG data processingom v doprave a telekomunikáciách.

 

The role of optimization in Machine Learning (Dominik Csiba)

Optimization is a key component of most of the machine learning methods. Current problems often feature large amounts of high-dimensional data fitted by a complex non-convex model, such as deep learning, which are very difficult to learn efficiently. Novel approaches arise every month to tackle these problems, but the training time is often still unsatisfactory. In this talk, I will present a few scenarios, where optimization is the main bottleneck – featuring questions such as: “What kind of problems are difficult for deep learning?”. The talk is aimed for general technical audiences, everybody is welcome!

 

Automatické budovanie modelov pre časové rady s aplikáciou v energetike (Ján Dolinský)

Ján Dolinský zo spoločnosti tangent.works vysvetlí, prečo je automatické budovanie modelov tak atraktívne pre priemysel a prečo sa v tangent.works rozhodli pretaviť vedomosti a skúsenosti do produktu, ktorý sa nazýva TIM – Tangent Information Modeller. Ján bude rozprávať aj o informačnej geometrii, ktorá umožnila vybudovať tento produkt.

 

Extrakcia diskriminačných kľúčových slov (Márius Šajgalík)

Márius Šajgalík momentálne robí PhD v odbore Inteligentné informačné systémy na FIIT STU v Bratislave. Jadrom prezentácie bude reprezentácia textových dokumentov vo forme kľúčových slov pre účely kategorizácie a nové architektúry neurónových sietí, ktoré možno využiť nielen na extrakciu kľúčových slov bez učiteľa.

 

Budovanie NLP klasifikátorov (Vlado Boža)

Vlado Boža momentálne robí PhD v bioinformatike, účí ML na FMFI a okrem toho pôsobí ako machine learning lead v Black Swan Rational a aktuálne aj v CEAI Slovakia. Príde porozprávať o budovaní NLP klasifikátorov – od logistic regression po deep networks, od riešenia Kaggle súťaží po riešenei aktuálnych problémov v CEAI. Hovoriť bude aj o distant supervision a získavaní trénovaích dát a ukáže nám niekoľko efektívnych trikov na uľahčenie roboty.

Máš nápad na prednášku? Ozvi sa nám na info@mlmu.sk