Practical conference about ML, AI and Deep Learning applications
Machine Learning Prague 2018
– , 2018
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
- 9 Workshops
- 2 Parties
Phenomenal Speakers
Practical & Inspiring Program
Friday,
Workshops
CEVRO Institut, Jungmannova 28/17, Praha 1
Registration –
Room 103 | Room 106 | Room 203 | Room 205 | Room 217 | |
---|---|---|---|---|---|
–
coffe break – |
Machine Learning in the Wolfram LanguageMark Braithwaite, Wolfram A dynamic and interactive workshop designed to take any user from a Wolfram Language beginner to being able to manipulate and use neural networks. Learn to utilise the existing library of neural networks for your own use using retraining, manipulation of those allows you to optimise them to a particular use-case. Already an expert in neural networks? Then delve into the pile of tools at hand to further edit or construct your own networks. Learn to use the Wolfram Language’s data import capabilities and Wolfram|Alpha integration to obtain data for use with the developed networks. All done within Wolfram’s notebook based development environment, allowing for easy manipulation and phased development of code. |
Recommender Systems at Seznam.czOndrej Javornicky, Jakub Drdak, Matej Jakimov, Stanislav Nowak; Seznam.cz Recommender systems are probably the most visible application of machine learning. They are literally everywhere on the Internet. Most of what you can see on our website Seznam.cz is now generated by some kind of recommender system. This workshop is an introduction into basic, but still very efficient, recommending methods such as matrix factorization and logistic regression with practical tips for evaluation and how to apply them in practice on a large scale. In the second part, you will have an opportunity to play with these methods on our dataset consisting of clicks from Seznam.cz homepage. |
Security in Internet of ThingsMartin Bálek, Adam Hanka, Tomáš Trnka, Galina Alperovich, Martin Neznal, Vladislav Iliushin; Avast Internet of Things (IoT) has become a hot topic in IT security. In early 2017, a vast majority of network security experts expected an increase in IoT breaches throughout the year, which underlines a very important point: nobody is immune to IoT attacks. Join us in Avast’s workshop on IoT security and experience the challenges of ML-based solutions in the security domain. We will provide an update on the current situation and guide you through the process of increasing security on your home network step by step. The main focus will be on the structure of network data and ML algorithms that can be used for device recognition and detection of anomalous behavior on IoT devices. Showing examples of network communication between clean and infected IoT devices, we will discuss the weaknesses and advantages of various ML methods within the context of IoT network traffic security, explaining the necessary steps in data preprocessing, and commenting on challenges in acquiring proper training data. |
Deep Learning for Text ProcessingPetr Baudis, Simon Pavlik; 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 and RNNs). You will get familiar with basic classification tasks performed on texts. Together we are going to build word level and character level models and use them to recognize entities in texts. We will be playing with some of the most popular tools like Keras and Tensorflow. |
Cloudera Data Science Workbench (1/2)Johny Darkwah, Gauss Algorithmic Getting ML models running in production environments at scale can be challenging. This leads to data scientist spending more time on pipelines and cluster administration than on data analysis. In our interactive workshop, we will introduce the state of the art in terms of developing end-to-end machine learning applications. Attendees will learn how to capture and process massive volumes of structured or unstructured data from cloud object stores as a single source of truth. Once our data is ready, we will demonstrate how to leverage on-demand cloud compute resources and the processing power of GPUs for model training, all on real-world use cases and examples. Finally, we’ll learn about visualization and validation options, as well as demonstrate how to move our model to production. Participants will use the Cloudera Data Science workbench, which allows them to bring their existing skills and tools in R, Python, and Scala to securely run computations on data in Hadoop clusters. It is a self-service collaborative, scalable, and extensible platform for that lets data scientists manage their own analytics pipelines. We will also use Cloudera Altus Platform-as-a-Service to process large-scale data sets stored in cloud object store. |
– |
|
||||
–
coffe break – |
Big Data Science – Elucidation and Practice with Spark AlgorithmsStephan Sahm, Data Reply In this workshop we are going to get hands on Apache Spark for doing Big Data Science. You will get a prepared environment with everything you need so that we can start coding some examples. Complementary to this practical part, we will look into the algorithms already implemented for spark, focussing on the crucial features which make these algorithms scale to very big data. This workshop gives you ground work in theory and practice to develop your Big Data Science with spark. |
Deep Learning for Music Classification using KerasAlexander Schindler, Vienna University of Technology Deep Learning has re-entered the field of Machine Learning with a big bang. Tasks that were difficult and cumbersome to solve are now simple to implement and achieve amazingly high accuracies. Deep Learning has shown substantial impact recently also in the domain of audio recognition and music classification. This tutorial will give a general introduction to Neural Networks, Convolutional Neural Networks, plus important concepts such as (batch-)normalization, training epochs, activation, loss, optimization, pooling, dropout, model fine-tuning, etc. and a tutorial on how to apply all of this on tasks of audio and music recognition using the convenient Python Keras framework on top of Tensorflow. It will use T-SNE for visualization and also cover unsupervised learning approaches such as Autoencoders as well as Siamese Networks. |
Air ticket relevancy predictionRoman Rožník, Martin Mokrý, Mojmír Vinkler, Martin Vo; Kiwi.com There are usually thousands of possibilities to get by plane from A to B (and back). We have to identify those that are convenient to our customers and get rid of the others. All that in startup company with biased, noisy or even missing data with little knowledge about customers. Insights into trying to do data science in crazy company. Hands on real data and real problems. |
Gensim: topic modelling for humansRadim Rehurek, Ivan Menshikh; RaRe Technologies Dig into the Python ecosystem for data science with a hands-on workshop. This workshop focuses on the open source tool Gensim, which is used for unstructured text processing by hundreds of companies around the world. The workshop is run by the maintainers of Gensim and requires a basic understanding of Python, but no prior knowledge of Gensim itself. We’ll cover the architecture and design of Gensim, its strong and weak points, tips for performance and building robust applications. Participants are expected to bring their own laptops, to follow the hands-on exercises. We’ll focus on vector embeddings, using the popular word2vec and fasttext algorithms. Dataset downloads will be specified in advance. |
Cloudera Data Science Workbench (2/2)Johny Darkwah, Gauss Algorithmic Getting ML models running in production environments at scale can be challenging. This leads to data scientist spending more time on pipelines and cluster administration than on data analysis. In our interactive workshop, we will introduce the state of the art in terms of developing end-to-end machine learning applications. Attendees will learn how to capture and process massive volumes of structured or unstructured data from cloud object stores as a single source of truth. Once our data is ready, we will demonstrate how to leverage on-demand cloud compute resources and the processing power of GPUs for model training, all on real-world use cases and examples. Finally, we’ll learn about visualization and validation options, as well as demonstrate how to move our model to production. Participants will use the Cloudera Data Science workbench, which allows them to bring their existing skills and tools in R, Python, and Scala to securely run computations on data in Hadoop clusters. It is a self-service collaborative, scalable, and extensible platform for that lets data scientists manage their own analytics pipelines. We will also use Cloudera Altus Platform-as-a-Service to process large-scale data sets stored in cloud object store. |
from |
Party La Loca Music Bar, Odborů 278/4, Praha 2 |
Saturday,
Conference day 1
Rudolfinum, Alšovo nábřeží 12, Praha 1
Registration –
Welcome to ML Prague 2018

Magic in the Machine
Anna Ukhanova, Google (CH)
Symbolic Neural Networks & Automated Machine Learning in the Wolfram Language
Etienne Bernard, Wolfram Research (USA)
Really Automating Machine Learning
Charles Parker, BigML (USA)Lunch

Personal Shopping Assistants for Conversational Commerce
Amit Srivastava, eBay (USA)
Near-Optimal Interactive Recommender Systems
Branislav Kveton, Adobe Research (USA)
Dimensionality Reduction for Guaranteed Display Advertising
Antonín Hoskovec, Seznam.cz (CZ)Coffee Break

Deep Learning is Revolutionizing Artificial Intelligence
Sepp Hochreiter, Institute of Bioinformatics, Johannes University of Linz (AT)
The Alexa Prize Socialbot
Jan Pichl, eClub Prague (CZ)
Prospects in Quantum Enhanced Machine Learning
Jacob Biamonte, Skolkovo Institute of Science and Technology (RUS)Coffee Break

Putting Research Into Practice: The Good, the Bad and the Ugly
Radim Řehůřek, RaRe Technologies (CZ)
GDPR & ePrivacy rule as an EU gift to non-EU technological competitors
Karel Vaculík, Gauss Algorithmic (CZ)Party
Royal, Vinohradská 48, Praha 2 Sunday,
Conference day 2
Rudolfinum, Alšovo nábřeží 12, Praha 1
Doors open

Computer vision technology in Konica Minolta’s Care Support Solution
Satoshi Kondo, Konica Minolta (JAP)
Open Science challenges for Machine Learning in Healthcare
Leonie Mueck, Public Library of Science (UK)
Deploying text-classification RNNs in a multinational bank
Vlado Boža, CEAi (CZ)Coffee Break

Calibration of the web browsing traffic
Jonáš Amrich, Jumpshot (CZ)
Social Good at Cloud Scale
Michael Lanzetta, Microsoft (USA)
Machine Learning for Better Understanding: Improving the Efficiency of a Data Center
Karel Macek, DHL (CZ)Lunch

SpaceKnow: Using deep learning to monitor Earth from space
Michal Reinštein, SpaceKnow (CZ)
Joyful Machine Learning Over Telco Data
Jan Romportl, O2 Czech Republic (CZ)
Taking down ML pain points
Marek Modrý, CEAI (CZ)Coffee Break
Panel discussion
Sepp Hochreiter, Jacob Biamonte, Radim Řehůřek, Michael LanzettaClosing 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, Praha 1
Workshops
CEVRO Institut
Jungmannova 28/17, Praha 1
Now or never Tickets
Early Bird
Sold Out
-
Conference days € 200
-
Only workshops € 100
-
Conference + workshops € 290
Standard Ticket
Sold Out
-
Conference days € 240
-
Only workshops (sold out) € 120
-
Conference + workshops (sold out) € 350
Late Ticket
Sold out
-
Conference days (sold out) € 280
-
Only workshops (sold out) € 140
-
Conference + workshops (sold out) € 410
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
La Loca Music Bar
Odborů 278/4, Praha 2
Saturday party 19:00
Royal
Vinohradská 48, Praha 2
Our Attendees What they say about ML Prague
Thank you to Our Partners
Platinum Partner
Gold Partners
Silver Partners
Party Partner
Media Partners
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