27-29 November, Vilnius

Conference about Big Data, High Load, Data Science, Machine Learning & AI

Early Bird Ends In:








Tetiana Hladkykh

Kharkiv Polytechnic Institute, Ukraine

Dmytro Zikrach

Kharkiv Polytechnic Institute, Ukraine

Tetiana Hladkykh Biography

Tetiana is a Senior Data Scientist with strong computer science background. Her area of scientific interest includes Data mining, Artificial Intelligence (genetic algorithms, neural network, and fuzzy logic), Mathematical Statistic, Computer Vision, Graph Theory, Game Theory and Computational mathematics. In 2007 Tetiana completed her PhD in Technical Science at The National Technical University “Kharkiv Polytechnic Institute”, where she was working on “Verification of electronic devices dynamic parameters based on K-valued simulation”.

Tetiana has 18 years of academic experience and is an author of more than 35 scientific publications in fields of Electronic circuit simulation and Applied problems of Artificial Intelligence, including her recent works on ANN for Adaptive Resonance Theory (K-Valued Adaptive Resonance Theory of Neural Networks for Analyzing Operability of Computing Devices, 2014) and Pattern Recognition (Anomaly Detection – Unsupervised Approach, 2016).

Additionally, Tatiana was a research advisor in more than 26 scientific projects and publications in Computer Vision, Multi-criterion optimization and Economic planning and forecasting.

Dmytro Zikrach Biography

Dmytro successfully combines knowledge in Mathematics and practical approaches in the Data Science area. He has two Master’s degree (in Mathematics, and Finance), and he has the Ph.D. degree in Mathematics. Dmytro has more than 50 scientific publications. His scientific interest lies in the area of Mathematical and Complex Analysis, Theory of Probabilities, Machine Learning and Predictive Analytics, Computer Vision and Time-series Analytics.

He is aspiring Data Scientist who actively develops skills pertaining to this area, a fast learner with good analytical skills who keeps on learning things. An avid reader who believes in conceptualized learning and is interested in mining information and insights. Dmytro has great experience and research in Statistical Learning, Predictive Analytics, NLP, Time Series Analysis, Artificial Intelligence and Recommender Systems.

Moreover, Dmytro often takes part in various Data Science community competitions, hackathons, and challenges.


Machine Learning for non-Data Scientist: Smart Decisions Game

The purpose of our workshop “Machine Learning for non-Data Scientist: Smart Decisions Game” is the presentation of a new, game-based, approach to the designing ML systems. During the workshop, participants will play a card game that allows modeling the decision-making process regarding the choice of an algorithm for solving a particular type of problem, and after that, they will be able to implement it in practice.


  • Introduction
  • Introduction to Smart Decisions Game
  • Smart Decisions Game Part 1
  • Smart Decisions Game Part 2
  • Preparing result report
  • Presentation team’s reports, summing up and determining winners
  • Q&A

Course objectives

Once available only to scientists, today machine learning (ML) is open to a much broader audience of software architects and engineers. Inspired by attribute-driven design (ADD) and Smart Decisions (a software architecture design game for big data), the authors are happy to present a new version of the game focused on designing ML systems.

In this participatory session, you will learn about designing the architecture for ML systems via series of gamified interactive exercises. We will simulate state-of-the-art ML design systems by analyzing both business and technical requirements, selecting the best matching algorithms, and teaching how to validate early design decisions using rapid prototyping techniques.

Attendees are encouraged to think of an ML use case and bring it to the meet-up for discussion.

Target audience

The workshop is intended for people responsible for Machine Learning without experience in Data Science, not only to data scientists or engineers.

This course is suitable for all:

  • Level 1 Description:

Introductory and overview material. Assumes little expertise with topic and covers topic concepts, functions, features, and benefits. Although you will get more out of the workshop if you are more experienced.

  • Level 2 Description:
    Intermediate material. Assumes knowledge and provides specific details about the topic.

Course prerequisites

A personal computer.

27 November, 2018


Venue to be confirmed


27 November, 2018


Venue to be confirmed