Machine Learning Prague 2016 Conference (April 23–24)

On this blog, we have been often preaching how Machine Learning has been expanding out of its historic habitat of academics and R&D labs into all kinds of industries by powering smart applications. One of the reflections of this trend can be seen in the number of conferences and events that are bringing together real life practitioners and business leaders that are looking for ways to incorporate Machine Learning in their core activities. This year we are witnessing the addition of Machine Learning Prague to that growing event list as possibly the first conference of its kind in Eastern Europe. We are also proud to announce BigML’s active participation at the event that will take place on April 23–24, 2016.

In the organizers’ words “This is not another academic conference. Our goal is to foster discussion between machine learning practitioners and all people who are interested in applications of modern trends in artificial intelligence. You can look forward to inspiring people, algorithms, data, applications, workshops and a lot of fun during both days as well as at the afterparty.”

In addition to our Co-founders Adam Ashenfelter and Poul Petersen, the speakers roster includes an impressive mix of companies ranging from the well-established (e.g. Facebook, Yandex, Avast, Microsoft) to the up-and-coming European start ups. You can browse the full program at your convenience, but here are some highlights of what you can expect to get out of it while also sampling some of the best brews Europe has to offer:

• Deep Learning and Intelligent Applications: Dr. Xuedong Huang will use these examples to illustrate how Microsoft is using Deep Learning in products and services, including Cortana, Skype Translator, and Project Oxford cloud services.
• Intelligent Personal Assistants: Jan Sedivy of CTU will discuss the basic architecture, challenges and use cases of the future intelligent assistants.
• Learning Representations for Drug Discovery: How to utilize gene expression measurements to characterize drugs and drug candidates for their on- and off-target activities, to predict treatments for new indications, and to highlight potential safety concerns by Matthew Tudor, MSD.
• Online Hyperparameter Tuning in Non-Stationary Environments: Jonas Seiler of Plista will present a method using global Bayesian optimization with Gaussian Processes to model certain kind of non-stationarities.
• Distributed Representations for NLP: Facebook’s Tomas Mikolov will cover recent distributed representation breakthroughs, a very hot topic both in academic research and in industrial applied machine learning.
• TR Discover: Chris Brew of Thompson Reuters to present a natural language interface for exploring linked datasets that maps keywords into an intermediate first-order logic representation and queries along with a built-in autosuggest mechanism.
• Recognizing Malware: Libor Morkovsky of Avast will showcase their distributed database engine that uses instance-based classification with sub-second query times to classify malware so as to generate rules to identify similar samples in machines of their customers.
• MatrixNet Applications at Yandex: Michael Levin will explain their proprietary machine learning tool with different learning modes, such as ranking, regression and classification based on gradient boosting over decision trees. Applications include web search, ad click prediction, and churn prediction.

If you are a Business Innovation Leader, Head of Analytics and a beer-lover, there is no better time for you to book your ticket to Prague. We hope to meet you in person and exchange the latest on inspiring examples of state-of-the-art solutions and practical applications of Machine Learning.