Practical conference about ML, AI and Deep Learning applications
Machine Learning Prague
– , 2021Buy 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
Ashish KapoorPartner Research Manager, 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.
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.
François CholletSoftware engineer, Google (Creator of Keras)
Creator of Keras (keras.io), a leading deep learning API, author of the popular textbook "Deep Learning with Python", software engineer at Google Brain and contributor to the TensorFlow machine learning platform.
Tomas MikolovResearch scientist, Facebook AI Research
Tomas Mikolov has been a research scientist at Facebook AI Research since May 2014 where he lead the popular fastText project. He is joining CIIRC and the Prague ELLIS unit full-time from April 2020. Previously he has been a member of Google Brain team, where he developed and implemented efficient algorithms for computing distributed representations of words (word2vec project). He has obtained his PhD from Brno University of Technology (Czech Republic) for his work on recurrent neural network based language models (RNNLM project). His long term research goal is to develop intelligent machines capable of learning to communicate with people using natural language.
Vojta JínaPrivacy Enthusiast, Apple
Vojta is a privacy enthusiast. While at Google, he helped to create AngularJS to simplify web development and make testing easier. These days, he is on a quest to solve machine learning with user privacy in mind, building intelligent products at Apple.
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.
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.
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.
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.
Jeremy JonasSenior Product Manager, McKinsey & Company
Jeremy Jonas oversees ‘KNOW’ Profiles and Expertise Search, the most-used product family at McKinsey & Company, with over 3 million internal profile views annually. These applications show professional profiles and help teams find appropriate colleagues for specific needs, much like an internal LinkedIn.
Working with the Firm’s Prague-based Data Science team, Jeremy oversees the development of innovative ML-driven approaches to enhancing Profiles. This includes suggesting topics of expertise to add to profiles, now being extended into recommending colleagues to the Firm’s many Practices for leadership roles.
He is also overseeing experimentation with feedback-focused chatbots, leading so far to 10x higher feedback rates than any approach previously used with the product family.
Felipe ViannaData Science Specialist, McKinsey & Company
Felipe is a Data Scientist engaging with McKinsey internal teams to develop Machine Learning components to their products. He is mainly involved in NLP and retrieval projects, including the development of models for Expert Profiling. Being an engineer, he also takes care of full production deployment and scalability of the models developed.
Filip DousekSenior Director of Augmented Analytics, Workday
Filip was the CEO at Stories.bi (Gartner Cool Vendor, acquired by Workday). Now he leads augmented analytics development at Workday. Previously an SAP Solution Architect, analytics pioneer and published author (Flock Without Birds).
Alejandro SaucedoChief Scientist, Institute for Ethical AI & Machine Learning
Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he leads the development of industry standards on machine learning bias, adversarial attacks and differential privacy. Alejandro is also the Director of Machine Learning Engineering at Seldon Technologies, where he leads large scale projects implementing open source and enterprise infrastructure for Machine Learning Orchestration and Explainability. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has delivered multi-national projects with top tier investment banks, magic circle law firms and global insurance companies. He has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America).
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 PevnyConsulting Scientist, 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.
Krzysztof RojekCTO, byteLAKE
Krzysztof is CTO at byteLAKE and associate professor at the Czestochowa University of Technology, Poland. He links byteLAKE’s business with the research and academic world. Krzysztof is a huge fan and a promoter of the ideas that can start their life in the research space and eventually land in the practical, real-life business applications. He gained his PhD+DSc degrees in Computer Science (Parallel Computing, GPGPU, self-adaptable codes, AI applications).
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.
Paweł RedzyńskiSoftware Engineer, dvc.org
Electronics engineer by education, a software developer by profession, deep learning enthusiast by heart. After a few years of software development, Paweł switched to work in the field of data science. He spend one-year helping Warsaw-based startup (Sports Algorithmics and Gaming) with video analysis of football trainings. Now he is somewhere in between both fields, creating tools for machine learning practitioners at Iterative.ai (creators of dvc.org). When he is not working, can be found trekking.
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.
Petr StanislavHead of Engineering at AI Centre, O2
Petr‘s mission is to make a life of the data scientist a little bit easier. He is responsible for development of the Data and Machine Learning platform in O2 AICentre. He also leads data and machine learning engineering team. Machine learning and data is his passion.
Until end of the last year he aslo served as researcher for Department of Cybernetics of Faculty of Applied Science as was also Teacher. There he worked for more than 8 years on research and development in the fields of artificial intelligence, speech technologies, natural language processing and web technologies.
He is currently waiting to possibility to defend his Ph.D. in Artificial Intelligence.
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.
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.
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.
Lena RampulaData Science Engineer, H2O.ai
Lena is a data science engineer at H2O.ai located in Prague. Her expertise is in Natural Language Processing (NLP). At H2O.ai Lena is working with potential customers on solving machine learning and AI use cases. She holds a bachelor's degree in Cognitive Science and Linguistics from the Hebrew University in Jerusalem and a double master’s degree in Computational Linguistics from Groningen University and Charles University in Prague.
Stefan PacindaSolution Architect, H2O.ai
History in operational research and service virtualization.
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.
Avik SenguptaVP Engineering, Julia Computing
Avik Sengupta is VP Engineering and head of Julia Computing's European headquarters in London. Avik is the head of product development and software engineering at Julia Computing, contributor to open source Julia and maintainer of several Julia packages. Avik is the author of Julia High Performance, co-founder of two artificial intelligence startups in the financial services sector and creator of large complex trading systems for the world's leading investment banks. Prior to Julia Computing, Avik was co-founder and CTO at AlgoCircle and at Itellix, director at Lab49 and head of algorithmic solutions at Decimal Point Analytics. Avik earned his MS in Computational Finance at Carnegie Mellon and MBA Finance at the Indian Institute of Management in Bangalore.
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.
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
Katarína Vlčková, 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
Registration 09:00 – 09:50
Welcome to ML Prague 2020
Defining and measuring intelligence: from developing task-specific AI skills to developing broad AI abilitiesFrançois 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 LearningVojta 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.
POSTER SESSION & LUNCH
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 adoptionStefan 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 voicePetr 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 datasetsJeremy 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 2020Alejandro 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 projectsPaweł Redzyński, dvc.org
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 (dvc.org, git-lfs, pachyderm.io, renku...) that can be used to tackle data project problems.
Exchange rate estimation of cryptocurrencies with FPGA and GPU acceleratorsKrzysztof 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 LearningUri 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 100Filip Dousek, Workday
One year ago, Prague-based Stories.bi 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 Stories.bi, how it is different and why it's called the next generation of BI&analytics.
AI Awards Ceremony 2019Michala 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 SystemsAshish 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 IntelligenceKarthikeyan 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 learningTomas 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 impossibleAnders 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 PredictionOr 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 AITomas 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.
POSTER SESSION & LUNCH
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 ApplicationsHamed 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 centerJohn 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 NetworksAdam 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 DiscussionAshish Kapoor, Microsoft
Karthikeyan Natesan Ramamurthy, IBM Research AI
François Chollet, Google (Creator of Keras)
Tomas Mikolov, Facebook AI Research
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
Radost FX Club
Bělehradská 234/120, Prague 2
Saturday party 20:15
Hybernská 1034/5, Prague 1
Our Attendees What they say about ML Prague
Thank you to Our Partners
Communities and Further support
Become a partner