Practical conference about ML, AI and Deep Learning applications

Machine Learning Prague 2019

, 2019

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.

  • 1 000+ Attendees
  • 3 Days
  • 45 Speakers
  • 8 Workshops
  • 2 Parties

Phenomenal Speakers

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 cfp@mlprague.com before December 10, 2018. Applications are chosen based on originality, impact, novelty, and comprehensibility, and presented in the form of posters at ML Prague 2019. Only selected applicants who purchase a ticket for ML Prague 2019 will be allowed to present their posters.

Practical & Inspiring Program

Friday,
Workshops

at CEVRO Institut, Jungmannova 28/17, Prague 1

Registration

Room 103 Room 106 Room 203 Room 205 Room 217
coffee break

Machine Learning in the real-world: why you need a platform to run Machine Learning at scale (1/2)

Poul Petersen, BigML

When data-driven organizations start using Machine Learning at scale (i.e. automatically creating and operating machine-learned models for dozens of different use cases) they soon realize how important it is to leverage a robustly-engineered platform that removes the complexities of Machine Learning and allows different stakeholders to focus on what matters most; enhancing and automating decision making.  In this workshop we will give you an extensive overview of the BigML platform and discuss the design principles that have been followed to remove the extra complexities that developing end-to-end Machine Learning applications typically implies while ensuring traceability reproducibility and many other features that mitigate the risks of operating hundreds thousands or even millions of machine-learned models in the real world. 

Quantum Enhanced Optimization and Machine Learning (1/2)

Jacob Biamonte, Skolkovo Institute of Science and Technology

This course introduces contemporary methods to utilize quantum processors to accelerate computational subtasks. No background in quantum mechanics is assumed. As part of a unified quantum programming framework we will introduce the core building blocks needed to program two types of quantum devices. (Type I) Quantum enhanced annealers; (Type II) gate-model based devices. We will compare and contrast the use of quantum annealers and gate-model based devices to (i) solve optimization problem instances; (ii) train binary classifiers. Course notes problem sets with solutions as well as compete programming examples will be provided.

Development of AI sound analysis method for automotive aftersales care

Jiří Dobeš, GoodAI Applied

Follow our journey from a good accuracy ML method to a very fine-tuned product with hands-on workshop.

Deep Learning for Text Processing

Petr Baudiš, Rossum

Join us for our workshop and get hands-on experience with Neural Network models used in Natural Language Processing (NLP). We will introduce you to the most common Neural Nets and techniques used in NLP (bag-of-words embeddings CNNs RNNs and autoencoders). Aside of basic text classification (similar to last year workshop) we will also talk about measuring text similarity as another task. We will be playing with some of the most popular tools like Keras and Tensorflow.

Machine Learning on the EDGE

Jan Pospíšil, Microsoft

Come to see how you can deploy and host ML model(s) on edge Raspberry Pi like devices. I will walk you thru full end-to-end deployment process using Azure IoT infrastructure. To “save” some time we will be using ML models from Custom Vision yet this scenario is definitely not restricted to those models only.   Workshop Environment: Visual Studio Code https://code.visualstudio.com/ Edge device (Raspberry Pi) or Linux virtual machine (Can be provisioned locally or on Azure as same as on any public cloud). Raspberry Pi should be running Raspbian https://www.raspberrypi.org/downloads/raspbian/ (Lite version recommended) VM should be running Ubuntu (16.04 or 18.04) https://www.ubuntu.com/#download Ideally have Azure environment (Free version is more than OK) https://azure.microsoft.com/en-us/https://www.raspberrypi.org/downloads/raspbian/ (Lite version recommended) VM should be running Ubuntu (16.04 or 18.04) https://www.ubuntu.com/#download Ideally have Azure environment (Free version is more than OK) https://azure.microsoft.com

Lunch
coffee break

Machine Learning in the real-world: why you need a platform to run Machine Learning at scale (2/2)

Poul Petersen, BigML

When data-driven organizations start using Machine Learning at scale (i.e. automatically creating and operating machine-learned models for dozens of different use cases) they soon realize how important it is to leverage a robustly-engineered platform that removes the complexities of Machine Learning and allows different stakeholders to focus on what matters most; enhancing and automating decision making.  In this workshop we will give you an extensive overview of the BigML platform and discuss the design principles that have been followed to remove the extra complexities that developing end-to-end Machine Learning applications typically implies while ensuring traceability reproducibility and many other features that mitigate the risks of operating hundreds thousands or even millions of machine-learned models in the real world. 

Quantum Enhanced Optimization and Machine Learning (2/2)

Jacob Biamonte, Skolkovo Institute of Science and Technology

This course introduces contemporary methods to utilize quantum processors to accelerate computational subtasks. No background in quantum mechanics is assumed. As part of a unified quantum programming framework we will introduce the core building blocks needed to program two types of quantum devices. (Type I) Quantum enhanced annealers; (Type II) gate-model based devices. We will compare and contrast the use of quantum annealers and gate-model based devices to (i) solve optimization problem instances; (ii) train binary classifiers. Course notes problem sets with solutions as well as compete programming examples will be provided.

Distributed Learning and You

Ruksi Laine, Valohai

We'll use common distributed learning libraries to speed up a machine learning training examples focusing on data parallelism and scalability of the training. After the workshop you will have the knowledge of why and how distributed learning can be used. Requirements: Laptop with Docker installed a dash of software development expertise and an open mind.

Analyzing social media data with Apache Spark using Python

David Vrba, Socialbakers

Apache Spark is popular computational engine used for big data processing machine learning and streaming. In this workshop we will take a deeper look how Spark can be used for analyzing social media data and building machine learning models. First we will spend some time with the DataFrame API and use it to answer some analytical questions about the data. Then we will explore and run some graph processing algorithms using Spark library GraphFrames that is built on top of DataFrames. In the next part we will focus on ML Pipelines - Spark native library for machine learning we will explore it's basic concepts such as Transformer Estimator and Pipeline and use it to build some ML models on the data.

Artificial intelligence and ethics

Ondřej Veselý, Kiwi.com

In the practical part of the workshop we'll learn how EU currently evaluates the ethical dimension of ML related R&D projects and how European AI Alliance prepares the roots for eventual AI regulations. In the light of medialised ML related ethical cases we'll discuss how it relates to our job and how we can avoid unethical decisions. Following part focuses on the ethics itself from different philosophical-sociological aspects. We'll identify and go through four basic narratives of AI discourse: scientific religious securitization and artistic.

from
Party
La Loca Music Bar, Odborů 278/4, Prague 2

Saturday,
Workshops

Rudolfinum, Alšovo nábřeží 12, Prague 1

Registration

Welcome to ML Prague 2019

Why inverse reinforcement learning is impossible, and why we can do it anyway

Stuart Armstrong

Most versions of IRL (inverse reinforcement learning) have assumed the human expert is rational, noisily rational, or, at best, fails to be rational is specific known ways. If the expert's rationality is completely unknown, however, then nothing can be said about their reward. More worryingly, simplicity priors don't help here - if we assume that the simplest interpretations are best, we get degenerate interpretations of human behaviour.
However, if we make a few a priori assumptions that cannot be derived from observations, we can do IRL successfully. One such assumption is that the mental models in human brains are informative about human preferences. This talk will illustrate how this can happen, and it's surprising connection with the "symbol grounding problem" - though applied to humans this time.

TBA

Solving the Text Labeling challenge with EnsembleLDA and Active Learning

Alexander Loosley

Want to build a text classification pipeline and have text with high quality labels that business can act on?  Great, throw in a language model, some BiLSTMs and CNNs and viola, you have trained a high-quality classifier. 

Unfortunately, many text data available for industry projects are unlabeled and difficult to label because of their industry specific nature.  The challenge can be split into three parts:

1. Unsupervised Text Exploration – What types of texts are there?
2. Label Curation – Given the texts, which set of labels provides the most business value?
3. Active Labeling/Learning – Which texts should be labeled first/next when human labeling is expensive?

This talk shares a few technical stories for solving all three challenges.

Datalore: Intelligent Web Application for Data Analysis

Vasily Korf

Intelligent and easy-to-use code editor with context-dependent suggestions, incremental recalculations, and built-in tools and libraries for machine learning.

POSTER SESSION & LUNCH

Parameter Servers Suck, All Hail Horovod

Ruksi Laine

Ruksi will discuss using our supreme overlord **data parallelism** to shard training data to achieve tenfold machine learning experimentation iteration speed. We'll go through the newest trends in distributed training, with some examples and highlighting common bottlenecks.

Machine learning: Explainablity with anti-models

Srivatsan Santhanam

Assisted or Supervised models of machine learning are a great boon to automate many mundane tasks in Enterprise world. Supervised model combined with hypothesis models helps in creating “situations” and in predicting possible outcomes to a given situation, thereby making “situations” intelligent.
But the question in user’s mind is how can I believe/trust what’s proposed is correct?
It’s here explainability becomes critical in ML. Inherently Decision Trees, Random Forest and features like boosting does help in showcasing significant factors and does trace the path of decision. LIME (Local Interpretable Model-Agnostic Explanations) does play a good role here. But for a user that’s just 50%. The remaining 50% is why the other “possibly close match” was not selected. It’s here the concept of anti-model is used.
The anti-model addresses the question of why the other “possibly close match” was not selected say in a classification scenario by building anti-models to the original models and its inference models to “explain” the decision taken. Multiple anti-models are constructed and validated with original model.
We will look at this ML approach, its benefits, applicability and potential success SAP has obtained in this space.
 

Spot the villain – The Merlon Identity Index

Dušan Fedorčák

At Merlon Intelligence, one of the CEAi portfolio startups, we are helping banks to fight money laundering by providing a system for screening of their potential customers. Currently, we process more than 10 TB of news articles and our main goal is to index the data for efficient search and risk identification. Although the process seems to be straightforward, there are many challenging problems hidden inside application of machine learning methods, in data annotation strategies or in performance and explainability requirements. I am going to talk about several of these issues and share our solution(s) from the first prototype to the production-ready system used in a very rigid banking environment.

COFFEE BREAK

Data-driven System health determination in Monitoring Softwares for Operational Intelligence

Smrati Gupta

The taxonomy of the different business problems from a data scientific perspective is relatively finite and it all starts with an accurate system health detection. However, the traditional methods of system health detection have practical limitations when monitoring performances of applications, infrastructure or network.  These limitations arise from the imposition of frequentist statistics based frameworks on common problems such as anomaly and outlier detection. In this session, we talk about some novel patented frameworks derived from quantum mechanics along with reinforcement learning algorithms to transition from the widely used frequentists statistics approach for system health detection to bayesian statistical approach. We also elucidate how at Broadcom we capture such frameworks to address multiple problems across lifecycle of monitoring softwares like anomaly detection, root cause analysis, predictions and prescriptions. 

ML powered Crime Prediction

Or Herman-Saffar

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 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.

Deep Neural Networks for Optical, Multispectral and Radar Satellite Imagery. Can GANs help us?

Jan Zikeš

In this talk we’ll demonstrate how SpaceKnow exploits the state-of-the-art deep learning techniques and the latest cloud technologies to generate insights and provide a global geospatial intelligence. We will focus mainly on applications related to the combination of optical satellite imagery, multispectral imagery and SAR. We will elaborate on how can recent models like GANs possibly help us.

COFFEE BREAK

Panel Discussion

Party

Sunday,
Conference day 1

Rudolfinum, Alšovo nábřeží 12, Prague 1

Doors open at

Solving the 3 main theoretical puzzles of Deep Learning

Tomaso Poggio

In recent years, artificial intelligence researchers have built impressive systems. Two of my former postdocs — Demis Hassabis and Amnon Shashua — are behind two main recent success stories of AI: AlphaGo and Mobileye, based on two key algorithms, both originally suggested by discoveries in neuroscience: deep learning and reinforcement learning. There is, however, little in terms of a theory explaining why deep networks work so well. In this talk, I will review an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. The class of deep convolutional networks represent an important special case that avoids the curse of dimensionality for the class of hierarchical locally compositional functions.

Artistic applications of artificial intelligence

Luba Elliott

Over the past couple of years, there has been increasing interest in applying the latest advances in machine learning to creative projects in art, music, theatre and beyond. From Google's DeepDream and style transfer to a GAN-generated painting selling for 400,000 USD at auction, more and more creative AI projects are moving beyond the world of research and academia into the public eye. This talk will give an overview of how artists and creative technologists are using machine learning and investigating its creative potential.

Machine Learning for recommender systems

Marc Romeyn

POSTER SESSION & LUNCH

TBA

Pavel Kordik

TBA

Leland McInnes

Spelling Correction for Web Search

Vladimír Kadlec

Web search engine almost never searches (only) for the words that users type in. Instead, during query processing phase, the engine tries to predict additional terms to search for.  This is especially important for misspelled queries.  The talk will provide details about how machine learning can be used to "read" user's mind for a correct version of the query. It is not a neuroimaging presentation but rather a brief history and current research of the spelling error correction system in the Seznam.cz's search engine".

COFFEE BREAK

Predicting the Global Economy with Automated Machine Learning for Dynamic Systems

Darko Matovski

The world is becoming more dynamic and real-time. We see more and more services becoming on-demand including food, transportation, delivery etc. This trend will continue to grow and become dominant. 
For example, there are more than 5 million drivers working for ride sharing companies spending 20 millions hours on the road every day. In order to allocate them more efficiently, ride sharing companies need vast amounts of accurate predictive models that can adopt to a changing and dynamic world for each one of the cities they operate in.
We will explain how AUTONOMOUS predictive technology, utilizing Automated Machine Learning for Dynamic Systems, can solve the problem at a more granular level than ever before. The way to solve it is by automating the whole pipeline of the construction of predictive models, from data preparation to feature extraction, feature set selection, hyperparameter tuning and performance evaluation of the models in Real-Time.

The Labels are Out There

Lotem Peled

Supervised Learning is all about the labels. From straightforward classification tasks to complex sequence to sequence models - we need labels in order to learn. Nowadays, some tasks have an abundance of labeled data available online, and for other tasks we usually just go for crowdsourcing. But what happens if both of these options are irrelevant? In this talk we will discuss exactly such cases, and present some creative ideas on how you can go along obtaining labeled data for your task.

TBA

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

Rudolfinum
Alšovo nábřeží 12, Prague 1

Workshops

CEVRO Institut
Jungmannova 28/17, Prague 1

Now or never Tickets

Early Bird

Sold Out

  • Conference days € 200
  • Only workshops € 150
  • Conference + workshops € 330

Standard Ticket

Late Ticket

From February 10

  • 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

Saturday party 19:00

Our Attendees What they say about ML Prague

Thank you to Our Partners

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Happy to help Contact

If you have any questions about Machine Learning Prague, please e-mail us at
info@mlprague.com

Organizers

Šárka Štrossová
sarka@mlprague.com

Jiří Materna
jiri@mlprague.com