The biggest European conference about ML AI and Deep Learning applications
running in person in Prague and online.
Machine Learning Prague 2022
In cooperation with Kiwi.com
– , 2022Tickets
World class expertise and practical content packed in 3 days!
At ML Prague 2022, you can look forward to another excellent lineup of 45 international experts in ML and AI business and academic applications. They will present advanced practical talks, hands-on workshops and other forms of interactive content to you.
Stay tuned. We will publish our full program with talks and 1-day, hands-on workshops soon!
What to expect
- 1000 Attendees
- 3 Days
- 45 Speakers
- 10 Workshops
- 2 Parties
Phenomenal First speakers announced
Hamed ValizadeganSenior Machine Learning Scientist, NASA
Holder of a PhD in computer science with focus on machine learning and data mining, Hamed Valizadegan joined NASA Ames Research Center (USRA) as a machine learning research scientist in 2013. At Ames, he has been involved with multiple projects including Automatic Planet Discovery (Kepler and TESS missions), Vascular Image Segmentation (Space Biology), Display Verification (Orion mission), and data driven prognostics (Hubble Space Telescope). Before joining NASA Ames, he spent three years at University of Pittsburgh conducting research in Medical Informatics. He has published more than 25 peer reviewed papers and been invited to many industrial level conferences as speaker and keynote speaker.
Daria StepanovaResearch scientist, Bosch Center for AI
Daria Stepanova is a research scientist at Bosch Center for Artificial Intelligence. Her research interests include knowledge representation and reasoning, machine learning and neuro-symbolic AI. Previously Daria was a senior researcher at Max Plank Institute for Informatics (Germany), where she was heading a group on semantic data. Daria got her diploma degree in Applied Computer Science from the Department of Mathematics and Mechanics of St. Petersburg State University (Russia) in 2010 and a PhD in Computational Logic from Vienna University of Technology (Austria) in 2015. Before starting her PhD she worked as a visiting researcher at the School of Computing Science at Newcastle University (UK) in an industrially-oriented project.
Anders SøgaardProfessor, University of Copenhagen
Anders Søgaard, born 1981, is a Full Professor in NLP and Machine Learning at the University of Copenhagen, as well as an ERC Starting Grant and Google Focused Research Award recipient.
Lenka ZdeborováProfessor, École Polytechnique Fédérale de Lausanne
Lenka Zdeborová is a Professor of Physics and of Computer Science in École Polytechnique Fédérale de Lausanne where she leads the Statistical Physics of Computation Laboratory. She received a PhD in physics from University Paris-Sud and from Charles University in Prague in 2008. She spent two years in the Los Alamos National Laboratory as the Director's Postdoctoral Fellow. Between 2010 and 2020 she was a researcher at CNRS working in the Institute of Theoretical Physics in CEA Saclay, France. In 2014, she was awarded the CNRS bronze medal, in 2016 Philippe Meyer prize in theoretical physics and an ERC Starting Grant, in 2018 the Irène Joliot-Curie prize, in 2021 the Gibbs lectureship of AMS, and the Neuron Fund award. She is an editorial board member for Journal of Physics A, Physical Review E, Physical Review X, SIMODS, Machine Learning: Science and Technology, and Information and Inference. Lenka's expertise is in applications of concepts from statistical physics, such as advanced mean field methods, replica method and related message-passing algorithms, to problems in machine learning, signal processing, inference and optimization. She enjoys erasing the boundaries between theoretical physics, mathematics and computer science.
Jan ŠedivýResearcher, CIIRC, Czech Technical University
Jan Šedivý has three decades of experience in the IT industry. He has led numerous global research and development projects and is the holder of 19 US patents. He began as a researcher and research manager at the IBM Thomas J. Watson Research Center (1992-2008), later moving to Google as a Technical Lead Manager (2008-2010). He subsequently returned to CTU-CIIRC, where he leads the NLP group, winning the Amazon Alexa Prize 4 in 2021 and two seconds and one-third place in previous contests.
Timo MöllerHead of ML, Deepset
Timo Möller is Co-Founder of deepset and Head of Field Engineering, where he is at the intersection of latest NLP technology and production use. He studied computational neuroscience, is an open-source fan and passionate NLP engineer. Currently he researches better evaluation metrics for Question Answering and continually improves out of domain performance of deepsets open-source framework Haystack at customer projects.
Yama Anin AminofData Scientist, Meta (formerly Facebook)
Yama Anin Aminof is a Data Scientist at Meta. In her previous role, she worked at MyPart, an Israeli startup in the music industry, developing algorithms and researching lyrical and musical song features. Yama is an activist both in the social world, fighting the violence against women and children, and in the technological world, giving tech talks and mentoring female developers through their first steps in the data science world. Yama has a B.Sc in Mathematics and Physics from Tel Aviv University where she also expresses her passion for music by playing the saxophone in the TAU Wind Band.
Ashley ScillitoeData science research engineer, Seldon.io
Ashley is a data science research engineer at Seldon, where he works on developing production-ready tools for drift, adversarial and outlier detection. Prior to joining Seldon, he spent a number of years as a Research Fellow at The Alan Turing Institute. Here, he explored the use of machine learning for tackling aerospace engineering problems, with a focus on explainability and uncertainty quantification. Ashley also completed a PhD at the University of Cambridge, and is a keen proponent of open-source software, regularly contributing to a number of libraries as well as mentoring for programs such as Google Summer of Code.
Yauhen BabakhinSenior Data Scientist, H2O.ai
Yauhen is a Kaggle competitions Grandmaster with a total of 9 gold medals in classic ML, NLP and CV competitions. He holds a Master’s Degree in Applied Data Analysis from the Belarusian State University. Yauhen has 7 years of experience in Data Science having worked in the Banking, Gaming and eCommerce industries. At H2O.ai his focus area is Deep Learning and Computer Vision, in particular. Yauhen has developed Computer Vision AutoML functionality for H2O.ai’s Driverless AI.
Sachin SharmaML Research Engineer, ArangoDB
Sachin is a Machine Learning Research Engineer at ArangoDB whose aim is to build Intelligent products using thorough research and engineering in the area of Graph Machine Learning. He completed his Masters’s degree in Computer Science with a specialization in Intelligent Systems. He is an AI Enthusiast who has conducted research in the areas of Computer Vision, NLP, and Graph Neural Networks at DFKI (German Research Centre for AI) during his academic career. Sachin also worked on building Machine Learning pipelines at Define Media Gmbh where he worked as a Machine Learning Engineer and Scientist.
Jakub NáplavaResearcher, Seznam.cz
Jakub is a researcher at Seznam.cz with current focus on improving web search relevance ranking. As a Ph.D. candidate at Institute of Formal and Applied Linguistics at Charles University in Prague, he developed several state-of-the-art systems for grammatical error correction. He also created a large and diverse grammatical error correction corpus for Czech.
Frederick BednarData Analyst, EBCONT
Having an economic & statistical background (Master degree in Management Science at WU Wien), Frederick Bednar has also gathered technical experience as a “data aficionado” for about two decades now. At EBCONT, he works as a Data Analytics and Data Science Consultant and is responsible for projects especially in Data Science, ML/DL, NLP & NER, but also BI consulting and IoT projects.
Julian-Thomas ErdödyExpert in deep learning, EBCONT
Julian Erdödy is considered an expert in the design of Deep Learning Models for smart applications in security as well as retail industries. Before joining EBCONT, he co-founded and led a Startup in Vienna for more than five years that successfully developed and deployed a computer vision engine creating situational knowledge from image and video data.
Clifford BednarData Analyst, EBCONT
Starting from Software Development & Engineering as well as Data Analytics, Clifford Bednar has evolved his interest in various Machine Learning & Deep Learning techniques. He is now successfully combining his experience at EBCONT in different projects.
Miklós TóthMachine Learning Engineer, AlphaGen
Over 15 years of IT experience, worked at large enterprises like Volkswagen and also small startups with focus on Artificial Intelligence, Big Data and Blockchain. In the last 5 years he has delivered Big Data and Machine Learning related courses for people working at multinational companies like Apple, Microsoft, American Express, Deloitte, Volkswagen, Audi and many more. Besides being a freelance trainer he is the Lead ML Engineer at Neuron Solutions.
He is the organizer of one of the biggest Machine Learning / Deep Learning Meetup Community in Hungary and also runs his own podcast "Machine Learning Cafe" together with Levente.
Levente SzabadosDozent, Frankfurt School of Finance & Management
- Deep tech leader, consultant and manager with special interest in artificial intelligence, cognitive sciences, data science and deep learning
- Long time "Startupper" and CTO
- Lecturer in applied Artificial Intelligence, tech leadership
- Public speaker with interest in Buddhist studies, comparative religious studies and cognitive science
Motto: "Mind is my vocation"
Personal interest: In depth study and research in the field of cognition, be it natural or artificial, interest in network and process based thinking, it's technical applications and connections with Buddhist thought.
Specialties: Artificial intelligence / expert systems, innovation leadership, natural language processing, IT system engineering, management, consultancy, personal coaching
Jan Russenior researcher, Emplifi
Jan Rus graduated in Computer Graphics at the Faculty of Applied Sciences, University of West Bohemia, Pilsen where he also worked as a Scientific Researcher for 5 years. For data compression research, he received the Best Suitable Commercial Application award in 2010.
After leaving academia, Jan became a founding member of the research team at Emplifi (formerly Socialbakers), where he currently works as a Senior Researcher.
At Emplifi, Jan is mostly responsible for the design, research and development of core product features exploiting big data analysis and machine learning techniques. Creation of concepts and bringing them from concepts to working prototypes and implementations.
When not working for Emplifi, Jan cooperates with various startups helping them to solve data-related problems. In his free time, Jan enjoys movies and virtual reality.
Peter Jungjunior researcher, Emplifi
Peter Jung got his master’s degree in artificial intelligence at the Faculty of Electrical Engineering, Czech Technical University in Prague. In that time, he was working in Heureka as a Python engineer and helped them with the first bigger machine learning project as a part of his diploma thesis. Currently he continues as a part-time PhD student at the same university and at Emplifi, he works as a Junior Researcher, where his main responsibility is to deliver natural language processing and computer vision oriented solutions. At Emplifi, he also hosts an advanced Python education group where people gather monthly and share their knowledge.
When he isn’t training models, he’s training Tobias, the chihuahua. He likes to travel and would like to speak French at some time.
Radovan KavickyPrincipal Data Scientist & President, GapData Institute
Radovan Kavicky joined Datacamp among its first employees (historically 1st Data Science Instructor from CEE region & is still historically the only one worldwide who have made successful transition from regular student to instructor and employee after being #1 worldwide @ Datacamp platform for nearly a year, back in 2017).
Radovan is Data Science Polyglot (R, Python, Julia ++more) and Data Science Veteran with over 10 years of experience in Data Science and Applied AI/ML Consulting & extensive knowledge in the area (Data Science consulting, education & community building with successful cooperation with global leaders within our industry, like f.e. H2O.ai, Anaconda or Tableau). Radovan is also co-founder of Slovak.AI (Slovak Research Center for Artificial Intelligence) and member of various international professional societies within our Data Science & AI/ML industry, like f.e. IEEE Computer Society, CLAIRE (Confederation of Laboratories for Artificial Intelligence Research in Europe), European AI Alliance (European Commission/Futurium), TAILOR network (Trustworthy AI - Integrating Learning, Optimisation and Reasoning), UDSC (United Data Science Communities), PyData Global Network, Global Tableau #DataLeader network & The Python Software Foundation (PSF).
Radovan is also Founder of PyData Slovakia/Bratislava (#PyDataSK #PyDataBA), R <- Slovakia (#RSlovakia), Julia Users Group Slovakia (#JUGSlovakia), SK/CZ Tableau User Group (#skczTUG) & Effective Altruism Slovakia (#EASlovakia) that you are all welcome to join.
Tomáš NeubauerCTO, Quix
Tomáš Neubauer is cofounder and CTO at Quix, responsible for the technical direction of the company across the full technical stack, and working as a technical authority for the engineering team. He was previously technical lead at McLaren, where he led architecture uplift for Formula One racing real-time telemetry acquisition. He later led platform development outside motorsport, reusing the knowhow he gained from racing.
Javier Blanco CorderoSenior Data Scientist, Quix
Javier Blanco Cordero is a Senior Data Scientist at Quix, where he helps customers getting the most out of their data science projects. He was previously a Senior Data Scientist at Orange, developing churn prediction, marketing mix modelling, propensity to purchase models and more. Javier is a masters’ lecturer and speaker, specializing in pragmatic data science and causality.
Robert BarcikEducator and trainer, Authentic Data Science
Robert Barcik is an independent educator and trainer at Authentic Data Science. Robert is originally a data scientist with a focus on human-related datasets. He delivered numerous use cases within the CEE region under the highly regulated banking industry (Raiffeisen Bank International). In recent years, he has focused on influencing the data-driven culture of organizations. His lectures reach thousands of students and nowadays revolve mainly around Machine Learning models.
Practical & Inspiring Program
at CEVRO Institut, Jungmannova 28/17, Prague 1 (workshops won't be streamed)
|Room 103||Room 106||Room 203||Room 205||Room 218|
Small data, great insights: ML/DL tricks with restricted data
Miklós Tóth, AlphaGen
With all the talk about big data in practical use cases we very often find ourselves in settings where specific data is very limited so we have to resort to special techniques to be able to train reasonably performing models. In this workshop - building on their hands on experience as well as theoretical reflections accumulated during teaching and mentoring - Miklós and Levente demonstrate a wide variety of techniques combatting "small data" and also along the way try to draw some conclusions about the generalization abilities of (deep) machine learning models as well as demonstrate with hands on exercises the application of these methods. Who is this for? Data scientists with practical experience who would like to broaden the palette of their tools and gain some theoretical insights as well.
ML in live data processing
Tomáš Neubauer, Quix
In this workshop you will learn how to use machine learning in real-time systems. You will process data live with a trained ML model with almost no latency. In 3 hour workshop you will get a chance to build PoC using Python from scratch with a team that worked in F1 racing processing car telemetry at a massive scale.
Text analysis with Apache Spark 3.x and Python
David Vrba, Emplifi
Apache Spark became a standard for data processing in a big data environment. It is well integrated with the Python programming language and the integration became even more emphasized in the 3.x releases. In this hands-on workshop we will see how Spark can be used for analyzing textual data using Spark SQL along with the native package for machine learning - Spark ML. We will also explore Spark NLP which is a state-of-the-art library for natural language processing that provides machine learning and deep learning capabilities for text analysis on top of Spark.
Language Model Essentials: Pre-training, Metrics, and Community
Nick Doiron, Hewlett Packard Enterprise
Learn the essentials to train fine-tune and patch language models with the Transformers library. In this workshop we will compare accuracy of masked language models on select tasks using architectures such as BERT and T5. For generative models (such as GPT-2) we explore the options to generate text through greedy search and beam search. In the end we will cover how to participate in the open source NLP community including sharing language models on HuggingFace and/or AdapterHub.
Synthetic Data Generation for Computer Vision
Frederick Bednar, EBCONT
Collecting reliable and properly labeled image and video data in sufficient quantities denotes one of the major challenges in computer vision still preventing many projects both in research and industrial domains from seeing the light of day. In this workshop we would like to show you how to generate and use synthetic datasets with the help of game engines in order to accelerate the image annotation process. We will augment our datasets using domain randomization techniques to simulate possible variations and scenarios in the real data. Finally we will use these datasets to train a neural network and demonstrate the benefit of this approach by measuring the network’s performance against real data.
Explainable AI/ML (XAI) in Python
Radovan Kavicky, GapData Institute
In this workshop led by Radovan Kavicky from Datacamp Basecamp.ai & GapData Institute you will get familiar with Explainable AI (XAI) and how to implement these principles in Python. Together we will open the "black box" of machine learning where sometimes even its designers cannot fully explain why an AI/ML arrived at a specific decision and also point out differences from statistical learning. We will learn how to better design systems that imitate intelligence in transparent way and you will also get an overview of current trends in Explainable AI/ML.
Recommendation systems and user representations
Radek Tomšů, Seznam.cz
Popularity of deep neural networks and embeddings in machine learning is transcending into the realm of recommender systems and is getting attraction within industry. Recommendation systems are used in many industries such as eCommerce social networks content providers and many more. They are improving user experience radically. In the theoretical part of the workshop we will go through different architectures of neural networks that are currently the state of the art in the recommendation domain. In the practical part we will train deep neural networks on our internal datasets and demonstrate benefits of various architectures and user features. In particular we will show how to employ a variety of user features to address the cold-start problem.
Reverse Image Search
Jan Rus, Emplifi
Find most similar images in the data given a reference image. We start with a simple baseline using ImageHash. Then show its limitations and proceed to a more robust solution using the latest DL models. With an adjustable threshold specifying how big differences are allowed. Along with fixes for edge-cases like completely black or white images.
Practical aspects of reinforcement learning deployment in business
Michal Kubišta, Dataclair.ai
Reinforcement learning models are a new type of intelligent machine that can help you drive your car or beat you in Starcraft. Let’s say you have successfully trained your model and it works on your machine. Now how do you make it useful to your colleagues? We will of course walk you through the process of building and training such models but our work(shop) will not stop there. How do we tune the hyperparameters? How can we deploy our solutions so other people can use them (get over the phrase “it works on my PC”)? How do we monitor the performance and compare different approaches? These problems can be as complex as building a deep neural network and cause many projects to fail before ever reaching the production stage. And that’s what we want to focus on.
The Future of AI in the EU is Trustworthy
Robert Barcik, Authentic Data Science
The realm of Machine Learning and Artificial Intelligence within the European Union is about to get more regulated. EU is continuing its work on its guidelines for Trustworthy AI. The guideline states that the models we build shall be lawful ethical and robust. During this workshop you will get an understanding of the essence of this regulation. On top of it we will explore and discuss how organizations and ML practitioners will need to adjust their approaches and processes to be compliant with the regulation. We will mix our discussions. On the technical side we will for example talk about the testing of ML models through techniques such as artificial alternations of the training dataset. On the less technical side we will for example uncover the necessity of adjusting for models that do not discriminate against humans unfairly.
Rudolfinum, Alšovo nábřeží 12, Prague 1 (and on-line)
To be announced
Conference day 1
Rudolfinum, Alšovo nábřeží 12, Prague 1 (and on-line)
To be announced
Have a great time Prague, the city that never sleeps
A unique capital where you can breathe centuries of history at every corner. We’ll take a tour to explore the sights, invite you to taste the best pivo (that’s beer in Czech) and bring you back to the present by clubbing with you the whole night!
Venue ML Prague 2022 will run hybrid, in person and online!
We'd like to bring to your attention that ML Prague comes back to the Rudolfinum Music Hall venue in 2022, and our workshops will take place at the Cevro Institute. So we will have the chance to enjoy the conference together again after 3 years!
We will also livestream the talks for all those participants who prefer to attend the conference online. Our platform will allow interaction with speakers and other participants too. Workshops won't be streamed.
Alšovo nábřeží 12, Prague 1
Jungmannova 28/17, Prague 1
Now or never Tickets
What You Get
- Practical and advanced level talks led by top experts
- 2 parties in the city with people from around the world. Let’s go wild!
- Traditional Czech food and snacks throughout the conference
They’re among us We are in The ML Revolution age
Machines can learn. Incredibly fast. Faster than you. They are getting smarter and smarter every single day, changing the world we’re living in, our business and our life. The artificial intelligence revolution is here. Come, learn and make this threat your biggest advantage.
Our Attendees What they say about ML Prague
Are you attending too? Do you have tips for what not to miss?February 27, 2021
Guys, job more than well done 👍 thanks for great conference🙂— Ivan Kasanický (@IvanKasanicky) February 28, 2021
Thank you to Our Partners
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Happy to help Contact
If you have any questions about Machine Learning Prague, please e-mail us at
Scientific program & Co-Founder