Data Science Summer School – 2016

Lviv Computer Science Summer School is an educational initiative from the Computer Science Program of the Ukrainian Catholic University. The Summer School participants – undergraduates, PhD students, young professionals – studies state-of-the-art methods and tools of computer science.

The school was held during July 10-23, 2016 and was about Data Science technologies. Data science and data analytics are important aspects of the modern informatics development that goes far beyond computer science. The job market in this field is developing rapidly – there are lots of new startups that arise each month in the world and use data science methods in different ways, and the top universities regularly open new academic programs in this field. The academic program will be conducted in English.

There were 52 participants from 7 countries. The academic program was conducted in English. The participants of the Summer School gained 4 ECTS credits.

Lviv Data Science Summer School 2016

Study program

The goal of the Summer School is to give the practice-oriented knowledge in the field of Data Science. The school’s program will incorporate three following stages. During the first stage participants will attend introductory courses that would make them familiar with the main theme of the school. During the second stage the students will be involved in several elective practice-oriented courses. Students can attend up to four courses from the proposed list (see below).

The third stage will be substantially dedicated to working on the projects. The project topics will be provided by the supervisors: school’s lecturers, representatives of IT companies and other partner organizations. The project work during several days means implementation and approbation of the previously obtained knowledge and skills. The project teams will present their results publicly at the end of the school.

 

Course list

 

Lecturers

The scientists from well-known European universities and the leading specialists of the Ukrainian and world’s companies will teach at the summer school.

Dr. Yarema Okhrin

Professor of Statistics
at the University of Augsburg, Germany

Dr. Greg Morrison

Statistical Physicist
at IMT School for Advanced Studies, Italy

Dr. Dimitri Nowicki

Computational neuroscientist
at Institute of Cybernetics of NASU, Ukraine

Сергій Шельпук

Sergii Shelpuk

Head of Data Science
at Ukraine QRhythm

Dmytro Fishman

PhD student in the field of Bioinformatics
at the University of Tartu, Estonia

Igor Kostiuk

Data Scientist
at Softserve, Ukraine

Дмитро Прилипко

Dmytro Prylipko

Machine Learning Engineer
at BuddyGuard UG, Germany

Андрій Газін

Andriy Gazin

Data Journalist & Analyst
at Texty.org.ua, Ukraine

Jordi Carrera Ventura

Jordi Carrera Ventura

Computational Linguist
at Quarizmi AdTech, Catalonia, Spain

Dr. Dmytro Karamshuk

Data scientist
at King’s College London

Mr. Viktor Sarapin

CEO
at V.I.Tech, Ukraine

Ms. Elena Sügis

Bioinformatics researcher
at the University of Tartu, Estonia

Dr. Oleksandr Baiev

Senior Engineer
at Samsung R&D Institute Ukraine

Dr. Rostyslav Hryniv

Professor
at University of Rzeszów, Poland

Mr. Alexander Dik

Big Data Consultant
at Sigma Software, Ukraine

Participants’ projects

  • English Spell Checker
    • The project goal was to create a state-of-the-art automatic spellchecking system using the most recent advances in the industry as well as traditional technologies
  • Ukrainian Spell Checker
    • The project goal was to create an automatic spell checking system for Ukrainian texts using the most recent advances in the industry as well as traditional technologies
  • Beautiful handwritten letters
    • The project goal was to build an end-to-end system that translates spoken language directly into handwritten text using automatic speech recognition, handwritten text generation, and a plotter
  • Neural artistic style
    • Deep dreams and the style transfer changed the way we use Deep Learning. Using CNNs to generate or modify images made deep learning very attractive not just to the researchers and engineers, but also to the artists
  • Analysis of real estate market through the lenses of geo-tagged social media data
    • The goal was to use the information about geographic location of points of interests (POIs), the self-reported locations of Foursquare users and other open source datasets to devise predictors of real estate prices in a city
  • Predicting ratio of cancerous cells in breast tissue images
    • The project goal was to apply state of the art machine learning and image processing methods for distinguishing between cancerous and normal cells based on images of breast tissue
  • Identifying autoantibodies associated with Alzheimer’s disease
    • The project goal was apply machine learning and bioinformatics algorithms including normalization and training a classifier in order to find most significantly differential proteins between Alzheimer’s patients and healthy controls
  • Similarity in US elections
    • In this project, students used US federal election finance data to assess how similar primary candidates are through the people that donate to them
  • iFace Facial Expression
    • Students developed an application for recognition of facial expressions
  • Photo tagger
    • The project goal was to create core for photo autotagging application. The system should describe input image by several tags which represent objects on the image
  • Photo organizer
    • The project goal was to create core for photo autotagging application. The system should describe input image by several tags which represent objects on the image
  • Real estate and demographic data analysis
    • The objective of the project was to collect and to analyze the real estate and demographic data for the US
  • Fasttext
    • The project goal was to speed up training models for Sentiment Analysis using hashing of n-grams

 

Impressions