Practical conference about ML, AI and Deep Learning applications

Machine Learning Prague

, 2021

Buy your ticket

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.

  • 1000 Attendees
  • 3 Days
  • 45 Speakers
  • 10 Workshops
  • 2 Parties

Phenomenal Speakers

Practical & Inspiring Program


at CEVRO Institut, Jungmannova 28/17, Prague 1


Room 103 Room 106 Room 203 Room 205 Room 206
coffee break

Zero to AI: Workshop on the Wolfram Language

Room 103

Mark Braithwaite, Wolfram

Designed by Wolfram data science experts this workshop will provide an introduction to machine learning techniques illustrated with live dynamic examples using the Wolfram Language. The workshop will walk you step-by-step through the basics of machine learning methodologies and techniques and how to apply them using the Wolfram Language. Upon completion you will come away with enough practical knowledge to immediately use the Wolfram Language for your own machine learning tasks on text data or images including supervised classification and prediction unsupervised feature identification sequence prediction and computer vision.

Developing Autonomous Vehicles with High Fidelity Simulation

Room 106

Ashish Kapoor, Microsoft

High-fidelity simulations can provide a rich platform to develop autonomy by enabling the use of AI technologies such as deep learning computer vision reinforcement learning etc. We have developed AirSim which is a simulator for autonomous vehicles built on the Unreal Engine. It is open-source cross platform and supports hardware-in-loop simulation thus allowing rapid development and testing of the system. The simulation is developed as a plugin and can be simply be dropped into any Unreal environment. AirSim supports AI development capabilities by exposing APIs to enable data logging and controlling vehicles in a platform independent manner. We will give an overview of how to use AirSim for building realistic simulation environments and doing development for quadrotors that use popular flight controllers such as Pixhawk. It is developed as a plugin that can simply be dropped in to any Unreal environment you want. We will also showcase how the system can be used to incorporate machine learning components useful for building such autonomous systems.

Cloud-native AI on OpenShift

Room 203

Marek Čermák, Red Hat
Václav Pavlín, Red Hat

Ever thought of doing a cloud-native AI work? What does that even mean? This workshop will introduce you to running AI related services like Spark Seldon or Jupyter on Kubernetes as part of a project Open Data Hub. You will learn how to move your AI workloads to the cluster and implement a basic data science workflow. As Jupyter notebooks have become the de facto standard in data science we will show you how to use them and adopt some of the best practices that we’ve developed over time.

How to Make Data-Driven Decisions: The Case for Contextual Multi-armed Bandits

Room 205

Katarína Vlčková, O2
Petr Stanislav, O2

Supervised learning has done wonders but it’s fundamentally limited. A good prediction of customers' churn or the likelihood of new acquisition may not always help you to do what is best in a given situation. By attending our workshop you will get hands-on experience with algorithms for direct optimization of decision-making with uncertainty. We will be focusing on the special case of reinforcement learning known as Contextual multi-armed bandit problems. Those problems arise frequently in important industrial applications played a role in AlphaGo success and are very often adopted by industry leaders such as Google and Netflix. Decision making with uncertainty is a challenge so we will show you how to effectively balance between trying new things to find better solutions and repeating the behavior that works well. During the workshop you will have an opportunity to play with a linear algorithm to solve a simple problem as well as with more advanced solution involving a deep neural network to learn a latent representational feature space for a problem.

SAS Viya & Open Source Integration focus on Python

Room 206

Ivan Kasanicky, SAS
Jordan Bakerman, SAS

In this course you will learn to use the Python API to take control of SAS Cloud Analytic Services (CAS) actions. You will also learn to upload data into the in-memory distributed environment analyze data and create predictive models in CAS using familiar Python functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You will then learn to download results to the client and use native Python syntax to compare models.

coffee break

Agile Data Annotation

Room 103

Marek "Marx" Grac, Phalanx

Come join us for our workshop and get hands-on experience with data annotation. The main goal of data annotation in Machine Learning algorithms is to make the implicit explicit so that the learning process can be improved. Even though many people see data annotation as a mundane task the process of creating guidelines and processes can be very interesting. In this workshop you will test various data annotation techniques mainly application-driven and low-cost approaches. We will also focus on how to measure the quality of the resulting data as well as test various UX principles and see how much they impact the cost-efficiency. Finally when you get bored of doing the manual part of data annotation yourself we will go through the basic legal aspects of outsourcing it.  

Automatic and Explainable Machine Learning with H2O

Room 106

Jo-fai (Joe) Chow, H2O
Lena Rampula,
Stefan Pacinda,

General Data Protection Regulation (GDPR) is now in place. Are you ready to explain your models? This is a hands-on tutorial for beginners. I will demonstrate the use of open-source H2O platform ( with both Python and R for automatic and interpretable machine learning. Participants will be able to follow and build regression and classification models quickly with H2O's AutoML. They will then be able to explain the model outcomes with various methods.

Machine Learning in Julia

Room 203

Kevin O'Brien, Coillte
Avik Sengupta, Julia Computing

Julia is specifically designed from the start of its conception as a language for high-performance computation but at the same time highly interactive. To achieve this Julia is one of the few modern languages that relies in just-in-time (JIT) compilation via LLVM to make its code run as fast or faster than statically compiled C and fortran codes. Its modern language design has the following features: multiple dispatch Lisp-like macros dynamic types type inference built-in parallel/distributed computing lightweight threads and elegant high-level language constructs. Outline: Introduction to Julia The Julia Language Julia in Data Science Julia Interfacing with Python and R Machine Learning in Julia High-performance computing in Julia  

Data Analysis in Big Data Environment with Apache Spark and Python

Room 205

David Vrba, Socialbakers
Peter Vasko, Socialbakers
Jiri Harazim, Databricks

Apache Spark became a standard for data processing and machine learning in big data environments and is popular especially for its high-level DataFrame API that allows working nicely with structured data in a very efficient way. In the first part of this workshop we will get familiar with the DataFrame API of Spark and see some challenges that you might face when processing large datasets. We will explore some advanced optimization techniques and see how to apply them to compose efficient analytical queries. In the second part of the workshop we will see how Spark can be used for machine learning and deep learning in particular. We will explore Deep Learning Pipelines - a library that integrates Spark with deep learning frameworks such as TensorFlow and Keras.

Programming the Pepper Robot

Room 206

Aleš Horák, Informatics at Masaryk University
Adam Rambousek, Faculty of Informatics at Masaryk University

The social robot by Softbank Robotics denoted as Pepper will be introduced. The robot hardware capabilities as well as examples of natural human-machine interaction in English and Czech (which are being developed by the team at FI MU) will be presented in detail including a tutorial on your own programming for a virtual or a real Pepper robot. The 1.2-m-tall robot is designed for social interactions with people and it is equipped with an extensive API set to detect faces mood or age and to react to their values.



Rudolfinum, Alšovo nábřeží 12, Prague 1

Registration 09:00 – 09:50

Welcome to ML Prague 2020

Defining and measuring intelligence: from developing task-specific AI skills to developing broad AI abilities

François Chollet, Google (Creator of Keras)

We all know we've been making fast progress in building increasingly intelligent artificial systems. Or have we? Although we are able to engineer systems that perform extremely well on specific tasks, they have stark limitations, being brittle, data-hungry, unable to make sense of situations that deviate slightly from their training data or the assumptions of their creators. By following a measure of AI progress entirely focused on task-specific performance, we've been very successful in developing systems that perform specific tasks... without featuring intelligence. To make progress towards developing generalizable, repurposable cognitive abilities as opposed as task-specific skills, we need precise, quantitative definitions and measures of intelligence – in particular human-like general intelligence. This talk presents a formal definition of intelligence as skill-acquisition efficiency, and a new benchmark dataset to measure it.

Predictive maintenance: How does data science revolutionize the world of machines?

Victoriya Kalmanovich, Israeli Navy

In today’s world of machines, there are two leading maintenance techniques to support a standard machine lifcycle. Predictive maintenance revolutionizes the future of machines. It tracks each of the machines’ unique lifecycle and doesn’t generalize. It allows us to know if our machines need to be attended to in advance. This maintenance method provides information on upcoming, unknown, or unpredicted critical failures and creates an effective and innovative environment. Using data science to solve predictive maintenance revolutionizes the way we look at machines. It changes the data collection approach, enhances its quality, and allows proper usage of the collected data.

Victoriya Kalmanovich shares a special maritime case study and discusses the big promise of predictive maintenance. In a world full of machines, we need to be the bridge connecting the methods of the past to the opportunities of the future.

Private Federated Learning

Vojta Jína, Apple

Federated Learning is a new approach that is picking up steam in the machine learning community as a way to improve global models by leveraging on-device training on user data. At WWDC 2019, Apple announced Private Federated Learning by combining Federated Learning with Differential Privacy. We have started to use this technology in iOS 13 for a variety of use cases, including QuickType keyboard, Found in Apps, and Smart Replies. In this talk, Vojta will provide more details about this technique.


Marketa Hulik Jansova, Tibor Zavadil: Automation of quality assessment of data from solar stations
Chandra Mohan Meena, Sarwesh Suman: Clustering Maturity of Machine Learning Platform Users
Rafał Bachorz, Małgorzata Mochol-Grzelak, Grzegorz Miebs: Efficient strategies of static features incorporation into the Recurrent Neural Network
Arun Mathew: SAP Behavioral Insights
Dominik Krzemiński, Matthias Treder: Symmetric filter pairs in convolutional neural networks
Adarsh Prasad Behera, Shekhar Verma, Manish Kumar: Manifold Visualization using Discrete Surface Ricci Flow
Andreea Mihailescu: Designing Predictive and Recommender Systems for Optimized Laser-Plasma Interaction Experiments. Implementations, Challenges and Future Prospects.
Stephan Baier: Analyzing Customer Feedback for Product Fit Prediction
Réda Alami: Memory bandits for decision making in non-stationary environment

Deep Learning for Medical Imaging – from breakthrough science to practical adoption

Stefan Braunewell, Konica Minolta

Deep learning has led to great progress in the field of medical image analysis. Following breakthrough results (“AI beats radiologists”) and a tremendous investment boom, the medical field has settled into skepticism and disappointment lately due to painfully slow adoption. We discuss challenges to medical AI adoption in the field of radiology and present our approaches to overcome these challenges, including coping with limited data and robustness in domain transfer.

How to build the perfect model of a human according to their voice

Petr Schwarz, Phonexia

Voice biometry is a technology that overperforms humans. Petr Schwarz will present how modern voice biometry systems are built and how they are deployed. The key issues are how to collect data, what are the input features describing the human vocal tract, what machine learning techniques are used for modeling, how to train the models, and how to deliver the model to its user while keeping the best accuracy.

Expertise recommendations - A supervised approach that surmounts incomplete datasets

Jeremy Jonas, McKinsey & Company
Felipe Vianna, McKinsey & Company

For knowledge-based organizations, finding precise expertise to address specific projects is increasingly important. At McKinsey we’ve been improving our internal expertise search capability, by enriching colleague profiles in various ways, including ML-driven recommendations for ‘Topics to call me about’. Through a number of innovations, our Prague-based Data Science team has created a highly-effective prediction model.
Traditionally, expert profiling and retrieval are based on document retrieval approaches. But can the information available in profiles be used to train a supervised model? As with many retrieval applications, our challenges began with a limited amount of data available, as well as the format, which at McKinsey is mostly PowerPoint files. Several well-known approaches were combined to perform a Document Classification step in unsupervised fashion, providing data to create the expert-candidate representations. In a later step, profiling of the experts was achieved despite noisy label data (incomplete profiles) and a large amount of features (compared to the amount of samples available).
Despite these challenges, the model is now achieving 80% acceptance of recommendations. In turn this is materially helping the Firm find appropriate experts when needed.


The state of production machine learning in 2020

Alejandro Saucedo, Institute for Ethical AI & Machine Learning

As the number of production machine learning use-cases increase, we find ourselves facing new and bigger challenges where more is at stake. Because of this, it's critical to identify the key areas to focus our efforts, so we can ensure our machine learning pipelines are reliable and scalable. In this talk we dive into key trends for production machine learning in 2020. These include machine learning deployment, interpretability, orchestration and concept drift.

Open source tools for versioning machine learning projects

Paweł Redzyński,

How can you make collaboration on machine learning projects more efficient? Is your team lost in a huge variety of data sources, versions of datasets, ML algorithms and hundreds of variations of hyper-parameters to try out? A collaboration of multiple contributors is not an easy task and can become chaotic, making the process of training models unpredictable and tiresome.

In this presentation, we will show, how data versioning and version control, in general, can help you manage your workflow. We will talk about why code version control systems are not enough for machine learning projects. We will present tools (, git-lfs,, renku...) that can be used to tackle data project problems.

Exchange rate estimation of cryptocurrencies with FPGA and GPU accelerators

Krzysztof Rojek, byteLAKE

Predicting cryptocurrency exchange rates is a significant issue in the economic market. A cryptocurrency is a digital asset designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. In current world, it gives serious effect on worldwide economic aspects. For the last years we observe an intensive development of accelerated computing platforms. Although current trends indicate a well-established position of GPU devices in the HPC environment, FPGA aspires to be an alternative solution to offload the CPU computation. This work presents a machine learning-based approach to predict the exchange rate of current cryptocurrencies in order to develop a better understanding on its price. Our goal is to compare the possibilities of utilization of platforms equipped with the FPGA and GPU accelerators. We focus on the set of aspects taking into account adaptation of ML methods to the accelerators, performance of the proposed solutions, accuracy of results, as well as the energy consumption of the platforms. This work investigates the most advanced accelerators used in the ML solutions, including NVIDIA Tesla V100 GPU, and Xilinx Alveo U250 FPGA. The exchange rate estimation is based on ML models using Long Short-Term Memory, as well as Neural Network with Hidden Layers.


The Ethical aspects of Machine Learning

Uri Eliabayev, Machine and Deep Learning Israel

Machine Learning has become a major part of our lives. As more and more companies and organizations implementing ML-based solutions, we need better understand the ethical aspects of Machine Learning algorithms.

In this talk, we will speak about the key element of this field (Fairness, explainability, bias and more) and give some past examples of ethical problems in the ML field. Alongside that, We will suggest ways to solve or reduce the ethical problem in each ML project and finally, we will learn how companies like Google and Microsoft make their algorithms fairer.

Building Augmented Analytics for 50% of Fortune 100

Filip Dousek, Workday

One year ago, Prague-based were acquired by Workday. Today, the same team is building its ML-driven augmented analytics for Workday's largest customers. Filip will talk about the concept behind, how it is different and why it's called the next generation of BI&analytics.


AI Awards Ceremony 2019

Michala Hergetová, Host

Come and enjoy the annual ceremony AI Awards of the Czech Republic. Meet the leaders of the AI community who are blazing the trail and pushing the boundaries of global AI research and development.

The Ceremony will be in Czech with translation to English.

Conference day 1

Rudolfinum, Alšovo nábřeží 12, Prague 1

Doors open at 08:30

Building Safety Mechanisms in Autonomous Systems

Ashish Kapoor, Microsoft

Machine Learning is one of the key component that enables systems that operate under uncertainty. For example, AI systems and robots might employ sensors together with a machine learned system to identify obstacles. However, such data driven system are far from perfect and can result in failure cases that can jeopardize safety. In this talk we will explore a framework that aims to preserve safety invariants despite the uncertainties in the environment arising due to incomplete information. We will describe various methods to reason about safe plans and control strategies despite perceiving the world through noisy sensors and machine learning systems. We will also consider extensions of these ideas, using high-fidelity simulation, to a sequential decision making framework that considers the trade-off in risk and reward in a near-optimal manner.

Understanding and mitigating unwanted bias in Artificial Intelligence

Karthikeyan Natesan Ramamurthy, IBM Research AI

AI and machine learning models are increasingly used to inform high-stakes decisions. Discrimination by AI becomes objectionable when it places certain privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage. In this talk, we will discuss the sources of unwanted bias in AI, and how it manifests along various points in the AI pipeline. We will also explore several methods of bias mitigation. Finally, we will discuss how bias can be measured and mitigated using the open source AI Fairness 360 toolkit.

Harnessing relational learning for explainable learning

Tomas Pevny, Avast

While most of the machine learning methods assume that samples are vectors, matrices, or sequences, in many real-world problems they have a rich structure. While this structure makes the manual design of features non-trivial, I see it as an inductive bias that should drive the design of models. In this talk, I will introduce a simple, yet powerful framework for learning on structured data. A side, yet important feature is the explainability of decisions, which is the result of ingesting data as-is instead of devising artificial features. A concrete implementation of the framework will be demoed on data from various stages of analysis of malware.


NLP is impossible

Anders Søgaard, University of Copenhagen

I will discuss the technical and philosophical foundations of NLP as a learning problem, with pointers to recent work in which we try to narrow the gap between the implicit assumptions often made in our research experiments and the unforgiving reality of doing NLP in the wild.

ML powered Crime Prediction

Or Herman-Saffar, Dell

What if we could predict when and where the next crimes will be committed? Crimes in Chicago is a publicly published dataset which reflects the reported incidents of crime that occurred in Chicago since 2001. Using this data, we would like not only to be able to explore specific crimes to find interesting trends, but also predict how many crimes will be taking place next week, and even next month.

Complex Systems for AI

Tomas Mikolov, Facebook AI Research

Machine learning has been tremendously successful in the last decade. The core concepts for training the models is to use supervision, error backpropagation and stochastic gradient descent. However, many scientists believe that to make steps towards more autonomous AI systems, we need to discover learning approaches that are fundamentally less supervised than the current ones. In this talk, I will describe a project where we attempted to define a system which can evolve for indefinitely long, possibly reach arbitrary complexity, and use no supervision. It is based on an old idea of cellular automaton which can be seen as a special type of a recurrent-convolutional network. I will show examples of interesting behavior that we did observe in automatically constructed models. We were able to discover these interesting automata using a novel metric which measures structured complexity growth in time. This work could be a basis of a new generation of machine learning models which can continue learning in interesting ways in situations where no supervision of even rewards are available.


Martin Holeček: Table understanding in structured documents
Jakub Slovan, Jan Rus, Luboš Andert and Petr Jančařík: Bayesian Social Media Content Inspiration
Christoph Briese, Erik Irmscher: Robust detection of non-cooperative aircraft using deep convolutional neural networks
Sebastian Eresheim: Cybersecurity Containment Agent
Gergely Szerovay, Laszlo Siller: An incluse open-source component library that bridges the gap in the application of machine learning tools
Martin Plajner: Generic system for promotional sales prediction from time series data and individual observations.
Jakub Bartel, Matej Choma, Vojtěch Rybář, Petr Šimánek: ML for High-Resolution Rainfall Forecast
Nóra Sándor, David Hagenmüller, Guido Pupillo: A machine-learning approach to mesoscopic Physics

Machine Learning For Space Projects: Example Engineering and Science Applications

Hamed Valizadegan, NASA

The success of the space projects depends much on our ability to understand and analyze their collected science and engineering data. In the science domain, the amount of collected data is so very large that requires building automatic tools to make sense of them. In this domain, often the data is not annotated well and/or there is not enough representative features for effective model construction. In the engineering domain, there are components that are designed and built for the first time and there is a limited domain knowledge available for them. This makes the hand-codes physics-based models less accessible and models that can utilize data for prediction more desirable. However, often there is a small number of working and failed units to learn from. In this talk, I provide examples of both cases and show how machine learning can help in these imperfect scenarios. First, I use our efforts in life time prediction of Fine Guidance Sensors of Hubble Space Telescope as an example to demonstrate how small amount of domain knowledge can help us develop effective machine learning models when the data availability is scarce. And then, I demonstrate our experience of using machine learning to classify the transit-like signals of Kepler spacecraft when annotation is imperfect and data features are not representative.

AIOPS, Machine Learning and Anomaly detection, our experience implementing a virtual assistant engine to detect and triage anomalous behavior in a data center

John Hart, Broadcom

Join this session to learn about our experience and challenges in designing a virtual assistant engine used in many of our IT data center monitoring products.  We will discuss both the use case we have solved, its evolution as we encountered challenges and some of the machine learning models implemented to solve these problems.  We will also provide in-depth review of some of the Dynamic Bayesian models we used to study non-normal time series in order to obtain the expected value of IT operations metrics like a SQL query processing time.  When anomalies are detected in the expected values, we perform some additional learning on these anomalies to detect patterns and auto-correlate live events in the data center. During the session, we will share both our learnings and challenges building this enterprise grade virtual assistant.

Confidence Estimation Learning for Production-ready Neural Networks

Adam Blažek, Iterait

Deploying your ML model to production may bring you headaches for many reasons, e.g. out-of-distribution input data or low-quality user input. Recognizing those cases is a crucial step for providing actionable feedback and handling those cases properly. This talk uncovers our simple yet effective recipe for integrated confidence estimation learning alongside a practical example used in a production environment.


Panel Discussion

Ashish Kapoor, Microsoft
Karthikeyan Natesan Ramamurthy, IBM Research AI
François Chollet, Google (Creator of Keras)
Tomas Mikolov, Facebook AI Research

Closing Remarks

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 pivos (that’s beer in Czech) and bring you back to the present by clubbing with you the whole night!

Impressive Venue

Now the seat of the Czech Philharmonic, the Rudolfinum is a Neo-renaissance building associated with music and art since 1885 and only used for truly outstanding purposes. The comfort, acoustics, and design makes it the greatest venue in the whole of Europe and it’s available for us.

Conference Hall

Alšovo nábřeží 12, Prague 1


CEVRO Institut
Jungmannova 28/17, Prague 1

Now or never Tickets

Early Bird

Sold Out

  • Conference days € 195
  • Only workshops € 150
  • Conference + workshops € 330

Standard Ticket

Late Ticket

Last 300 tickets

  • Conference days € 280
  • Only workshops € 195
  • Conference + workshops € 450

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 throughout the conference

We Know That A Little Party Never Killed Anybody

Friday party 19:00

Radost FX Club
Bělehradská 234/120, Prague 2

Saturday party 20:15

Červený Jelen
Hybernská 1034/5, Prague 1

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Šárka Štrossová

Jiří Materna