Practical conference about ML, AI and Deep Learning applications
Machine Learning Prague 2020
– , 2020Buy 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.
- 1 000+ Attendees
- 3 Days
- 45 Speakers
- 10 Workshops
- 2 Parties
Ashish KapoorSr. Principal Researcher, Microsoft
Ashish Kapoor leads the Aerial Robotics and Informatics group at Microsoft, Redmond. Currently, his research focuses on building intelligent and autonomous flying agents that are safe and enable applications that can positively influence our society. The research builds upon cutting edge research in machine intelligence, robotics and human-centered computation in order to enable an entire fleet of flying robots that range from micro-UAVs to commercial jetliners. Various applications scenarios include Weather Sensing, Monitoring for Precision Agriculture, Safe Cyber-Physical Systems etc. Ashish received his PhD from MIT Media Laboratory in 2006.
Hava SiegelmannProgram manager, DARPA
Dr. Hava Siegelmann joined DARPA in July 2016 with the goal of developing programs that advance intelligence in computerized devices, focusing on life-long learning, context-aware adaptivity, and user-centered applications. Prior to joining DARPA, she directed the Biologically Inspired Neural and Dynamical Systems (BINDS) Laboratory at the University of Massachusetts. While at the University, she also served as a Core Member of the Neuroscience and Behavior Program. Her mathematical and computational studies of the brain, somatic cells, cognition, and intelligence depend on a multi-disciplinary approach that combines complexity science, information and learning theories, computational simulations, biology, and neural networks. A unifying theme of her work has been the study of time-dependent adaptive dynamical complex systems. One of her research goals involves further investigation of how an underlying architecture brings about the dynamics that evolve into intelligent behavior and how behavioral feedback from the dynamics proceeds toward adaptation in the architecture. Her research accomplishments include advancing the understanding of biologically-inspired computational systems, among them neural systems and genetic networks of organisms.
Anders SøgaardNLP and ML Professor, University of Copenhagen
Anders Søgaard 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.
Susan SchneiderDirector of AI, Mind and Society Group, University of Connecticut
Susan Schneider is the NASA/Baruch Blumberg Chair at the Library of Congress and the director of the AI, Mind and Society Group at the University of Connecticut. Her work has been featured by the New York Times, Scientific American, Smithsonian, Fox TV, History Channel, and more. Her two-year NASA project explored superintelligent AI. Previously, she was at the Institute for Advanced Study in Princeton devising tests for AI consciousness. Her books include The Language of Thought, The Blackwell Companion to Consciousness, and Science Fiction and Philosophy.
Victoriya KalmanovichR&D Group Lead, Israeli Navy
Victoriya Kalmanovich is an R&D group lead at the Navy's technological unit. She specializes in healing work environments, promotes and leads innovative and broad processes throughout the organization. In her day-to-day experience, she deals with all technological issues, product management, budgets and client handling of her group. She is an education enthusiast, and often uses educational directives as a part of her management strategies, especially group members guidance and leadership.
She is a firm believer of deploying data science where there's a great value for data. She has organized a successful data science hackathon and is forming a data science community within the organization.
Karthikeyan Natesan RamamurthyResearch Staff Member, IBM Research AI
Karthikeyan Natesan Ramamurthy is a research staff member at IBM Research. His broad interests include understanding the geometry and topology of high-dimensional data and developing theory and methods for efficiently modeling the data. He has also been intrigued by the interplay between humans, machines, and data and the societal implications of machine learning. He holds a PhD in electrical engineering from Arizona State University.
Or Herman-SaffarSenior Data Scientist, Dell
Or Herman-Saffar is Senior Data Scientist at Dell. As part of her role, she designed various data science projects, from exploratory data analysis to application of machine learning models. Focus mainly on the following domains: feature engineering, time-series analysis, classification models. Or holds an MSc in biomedical engineering, where her research focused on breast cancer detection using breath signals and machine learning algorithms, and a BS in biomedical engineering specializing in signal processing.
Uri EliabayevAI Consultant, Founder, Machine and Deep Learning Israel
Uri Eliabayev is a business consultant in the field of AI. Uri has worked with many consulting companies and organizations and helped them to choose and implement the best AI solution for their needs. Moreover, Uri has found the biggest AI community in Israel called “Machine and Deep Learning Israel”.
John HartSenior Data Scientist, Broadcom
John Hart is a Senior Data Scientist at Broadcom working on their AIOps software solutions. John and the AIOps machine learning team are focussed on building intelligent automation systems that are self healing with minimal human intervention. John graduated with a PhD in Mathematics from the University of Montana. The focus of his dissertation was on non-commutative algebraic geometry. He recently made the switch from academics to the software industry, where he is currently researching applications of algebraic topology and non-commutative geometry can be used in unsupervised learning. Prior to that John spent several years teaching at Universities across the world in the Americas and Asia.
Phillip HunterSenior Principal Designer, 7 AI
Phillip is a technology app and product design leader at 7 AI with over 25 years of successful experience. He's filled pivotal design roles in consumer voice and multimodal applications at Amazon Alexa, Microsoft Cortana, and four start-ups. Phillip also led product design efforts for Amazon Web Services and other enterprise software products. He is passionate about how teams work, how to understand, influence, and create complex systems, and the little details of product design that make big differences.
Stefan BraunewellManager Digital Healthcare R&D, Konica Minolta
Stefan has been driven by the urge to bring technology into medical practice. He now leads an innovation lab for Konica Minolta, that aims to bring together user understanding and deep technological knowledge. The lab is applying deep learning to complex medical data such as images and -omics to create novel precision medicine solutions. Stefan holds a PhD in theoretical physics.
Tomas PevnyResearcher, Avast
Tomas has received his PhD in 2008 in University of Binghamton, SUNY, USA, where he has pioneered the use of Machine Learning techniques in Steganography and Steganalysis, for which he was awarded by IEEE Signal Processing Society. After one year post-doc in Grenoble, France, he has returned to Artificial Intelligence Center at Czech Technical University, where he has extended his interests to machine learning problems in Cybersecurity. He was closely working with Cognitive Security startup acquired in 2013 by Cisco systems Inc. Since September 2019 he is with Avast and with Artificial Intelligence Center at CTU.
Petr SchwarzCTO and co-founder, Phonexia
Petr Schwarz, PhD, is the CTO and co-founder of Phonexia. He helped to build the well-known research group Speech@FIT at Brno University of Technology, Czech Republic, worked as a researcher at Oregon Graduate Institute in Portland, OR, USA, and founded Phonexia in 2006. He participated in the development of multiple speaker recognition and language identification systems evaluated by the United States National Institute of Standards and Technology. Petr was also a team member on several Johns Hopkins University summer research workshops in the field of human language processing, and he is the co-author of several open source software projects. He has worked on several European, USA, and Czech research projects, and is the author or co-author of dozens of impactful research articles.
Adam BlažekCEO and co-founder, Iterait
Adam is a CEO and co-founder of Iterait, a company delivering computer vision AI solutions. As a leader of a research team at IBM and Cognexa, he gained experience primarily in healthcare-oriented projects. Adam has been publishing articles in scientific journals since his university studies at Charles University, where he graduated in Artificial Intelligence & Theoretical Computer Science. He received multiple awards for his Diploma thesis by the faculty’s Dean or in IT SPY competition.
Aleš HorákAssociate Professor, Informatics at Masaryk University
Aleš Horák is an Associate Professor of Informatics at Masaryk University, Brno, Czech Republic. His research concentrates on natural language processing, knowledge representation and reasoning, e-lexicography and corpus linguistics.
Adam RambousekResearch Assistant, Faculty of Informatics at Masaryk University
Adam Rambousek is a Research Assistant at the Faculty of Informatics at Masaryk University, Brno. His main research topics include computational lexicography, corpus linguistics, ontologies, and semantic networks.
David VrbaData Scientist, Socialbakers
David works as a data scientist at Socialbakers. He is using Spark on daily basis for processing data on different scales from few GBs up to tens of TBs. He also does query optimizations and helps with productionalizing of various ETL pipelines. David enjoys preparing and lecturing Spark training and workshops and trained in Spark already several teams such as data engineers, analysts and researchers. David received his Ph.D. from Charles University in Prague in 2015.
Václav PavlínArchitect/Principal Software Engineer, Red Hat
Vašek is now part of the Office of the CTO team at Red Hat working on enablement of AI/ML workloads on Kubernetes where he leads a project Open Data Hub. He has extensive experience with building, deploying and managing containerized applications on OpenShift/Kubernetes. He loves open source and openness as well as meeting new people and arguing about technologies.
Marek ČermákScientific software engineer, Red Hat
Among other things, Marek is a data engineer, visualization lover, an open-source enthusiast and a Jupyter hacker. He likes new technologies and the challenges they bring along. He also likes to write and/or talk about the solutions. Marek is currently working as a scientific software engineer at Red Hat in R&D.
Katarína VlčkováData Science Team Lead, O2
Katarína leads a team of data scientists focusing on the financial sector and customer behavior analysis. During her career in O2, she worked on various projects, including credit risk prediction and product recommendation. Currently, she focuses on the development of risk prediction with various machine learning techniques.
Katarína received her master’s degree in Statistics after graduating from the Faculty of Mathematics and Physics, Charles University in 2015. During her studies, she worked as a Research Assistant at the Institute of Computer Science, Academy of Sciences of the Czech Republic.
Jan MalýData Scientist, O2
Honza is passionate about AI and its potential to transform business. He loves to solve business challenges through better data understanding and machine learning algorithms. Honza believes in continuous and online learning as a way to overcome issues with traditional machine learning and does his best to apply those techniques in the real world. Currently, he helps leverage Deep Learning in several challenging problems. Honza received his master's degree in Computer Science at CTU.
Jordan BakermanSr. Analytical Training Consultant, SAS
Jordan Bakerman holds a Ph.D. in statistics from North Carolina State University. His dissertation centered on using social media to forecast real world events, such as civil unrest and influenza rates. As an intern at SAS, Jordan wrote the SAS Programming for R Users course for students to efficiently transition from the R to SAS using a cookbook style approach. As an employee, Jordan has developed courses demonstrating how to integrate open source software within SAS products. He is passionate about statistics, programming, and helping others become better statisticians.
Ivan KasanickyData Scientist, SAS
Ivan is an experienced Data Scientist with 9 years of experience. He has obtained Ph.D. degree in Probability and Mathematical Statistics from Charles University. During his career, he has work on different projects for, e.g., utility, transportation and automotive companies. He is an author of many advanced analytical models, such as predictive model for high way parking lots occupancies or renewable energy forecasting model. Ivan has joined SAS with a mission to help its customers to uncover how modern analytical and AI solutions can speed up their business. He focuses on understanding SAS customer business needs, and on showcasing how these needs and problems can be addressed with SAS advanced solutions.
Kevin O'BrienData Scientist, Coillte
Kevin O'Brien is Coillte's Forestry Resource Modeller, based in their offices in Limerick. Kevin has been very active in the data science community over the past decade, and is now a director of Python Ireland, the Community lead for Forwards: The R Foundation taskforce on women and other under-represented groups, a European R User Meeting conference committee member, and Social media chair of JuliaCon. He was formerly a Mathematics and Statistics lecturer at the University of Limerick.
Mark BraithwaiteTechnical Specialist, Wolfram
Mark Braithwaite is a primary instructor of Wolfram technologies for Wolfram Research Europe and teaches frequent classes on Mathematica and programming in the Wolfram Language. As a technical specialist with Wolfram Research since early 2017, Mark's areas of interest are ever-changing, as they are influenced by the latest developments in the Wolfram technology stack. He uses the Wolfram Language daily as a development tool for company and customer applications.
Mark is the author of several Wolfram U courses and gives talks and technical demonstrations on applications of the Wolfram Language in machine learning, AI and data science.
Jo-fai (Joe) ChowData Science Evangelist, H2O
Jo-fai (or Joe) has multiple roles (data scientist / evangelist / community manager / customer success manager) at H2O.ai. He is best known as the H2O #360Selfie guy nowadays. On Twitter, he sounds like a die-hard MATLAB fanboy with the handle @matlabulous (because MATLAB was his favourite tool at Uni). Since joining H2O.ai in 2016, Joe has delivered H2O talks/workshops in 40+ cities around Europe, US, and Asia. He is the organizer of London Artificial Intelligence & Deep Learning meetup - one of the biggest data science communities in Europe with 9500+ members.
Present your project!
Present your achievements in academic research or commercial applications in Machine Learning to a wide audience of experts in the field and discuss your current work in depth. Send us your abstract in PDF (max length 2000 characters) to email@example.com before January 15, 2020. Applications are chosen based on originality, impact, novelty, and comprehensibility, and presented in the form of posters at ML Prague 2020. Only selected applicants who purchase a ticket for ML Prague 2020 will be allowed to present their posters.
Practical & Inspiring Program
at CEVRO Institut, Jungmannova 28/17, Prague 1
|Room 103||Room 106||Room 203||Room 205||Room 206|
Zero to AI: Workshop on the Wolfram Language
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
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
Marek Čermák, 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
Jan Malý, 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
Ivan Kasanicky, 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.
Agile Data Annotation
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
Jo-fai (Joe) Chow, H2O
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 (https://www.h2o.ai/products/h2o/) 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
Kevin O'Brien, Coillte
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
David Vrba, Socialbakers
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
Aleš Horák, 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
Stay tuned! We're still working on this year's program with our speakers.
Conference day 1
Rudolfinum, Alšovo nábřeží 12, Prague 1
Stay tuned! We're still working on this year's program with our speakers.
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!
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.
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 throughout the conference
We Know That A Little Party Never Killed Anybody
Friday party 19:00
Saturday party 19:00
Our Attendees What they say about ML Prague
Thank you to Our Partners
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