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Traditionally, every summer we invite the best industry specialists, experts, mentors, and lecturers from leading IT companies to create a unique educational platform for learning the features and innovations of artificial intelligence technologies.

– to create an educational platform for studying theoretical bases and practical skills between students and the IT industry in the field of artificial intelligence.
– to increase the attention of students of technical specialities to the challenges and opportunities provided by artificial intelligence technology.
– to create the basis for the development and creation of innovative products and solutions using artificial intelligence.

Artificial intelligence is a powerful driver of change that opens up endless possibilities for the development of new technologies.

When: June 24 - July 7, 2024

Format: Online

Training features: there will be two parallel learning streams (CoreA - pupils and 1-2 year students and those, who are just trying to get into AI; CoreB - participants with experience in IT, including 3-4 year students).

School language: English

Target audience: Pupils and technical students from Ukraine and abroad

School Topics: machine learning, Python basics, deep learning, mobile development for iOS, Android, etc

For who we are looking (or need):
– Speakers, mentors, tutors for the school's work
– partners for quality representation of the school

Write to our inbox: marketing@remove-this.lnu.edu.ua

P.S. By creating artificial intelligence, we are opening up endless possibilities for a future where machines and humans can work together to achieve incredible success in science, art, and life in general," - chat GPT!


Python for DS, ML&DL

1. Python for data analysis
2. Python for visualization
3. Python for data mining
4. Version Control System basics:
Git, Data Version Control (DVC),
Data sources (Kaggle, etc.),
ML Hubs (Hugging Face, etc.)

Cloud services and technologies.
Cloud computing

1. Cloud computing basics,
SaaS, PaaS and IaaS
2. Amazon Web Services
3. Google Cloud Platform
4. MS Azure

Big Data

1. Big Data in AWS
2. Big Data in GCP
3. Big Data in Azure
4. Big Data Visualization

Deep learning

1. Deep learning basics
2. Supervised deep learning
3. Reinforcement deep learning
4. Unsupervised deep learning

Advanced Мachine learning

1. Image recognition and classification
2. Speech recognition and Audio recognition
3. Text recognition and text emotion detection
4. Deep learning for forecasting

Databases & Data warehouses

1. Database basics, relational, non-relational,
distributed databases
2. Data warehouse, ETL, Data Workflows
3. NoSQL: Key-Value, Column-based,
Document-based, Graph databases
4. Database usage for Data Science,
Data Analysis and Machine Learning

Мachine learning basics

1. Machine learning basics
2. Supervised Machine Learning
3. Reinforcement Machine Learning
4. Unsupervised Machine Learning

Machine Learning tools

1. Basic libraries: Numpy, Pandas, Scikit-learn,
Seaborn, matplotlib, sktime, skforecast
2. ML Frameworks (part 1):
TensorFlow, Keras, PyTorch
3. CV Libraries and frameworks, OpenCV
4. NLP libraries and frameworks, NLTK

Generative AI

1. Generative models basics
2. Generative models for artificial art
3. LLM, Transformers, BERT,
GPT models family (GPT-1, 2, 3, 3.5, 4)
4. LLM fine-tuning techniques

Real-world AI applications

1. ChatGPT usage, ChatGPT API,
tokenization, creation and usage GPTs
2. Artificial Intelligence of Things,
Embedded AI, AI Autonomous
Systems (drones, vehicles, etc)
3. AI Code Generation, Copilot, etc


Advanced Мachine learning

CV, Image recognition and classification, NLP, Speech recognition, Audio recognition, Text recognition and emotion detection, Deep learning for forecasting, End-to-end Machine learning projects/models to solve practical problems, Generative models, artificial art, Language models, Transformers, BERT, GPT models family (GPT-1, 2, 3, 3.5, 4), GPTs: Usage and creation, Adaptive AI, AI Trust, Risk and Security Management (AI TRiSM), AutoML, Multi-modal learning, Democratized AI

High-performance computing

Fundamentals of parallel, hybrid and distributed computing, Getting Started with Jetson Xavier, NX Developer Kit, Getting started with Google Coral's, TPU USB Accelerator or/and Google Coral, Development Board

Mobile development

Android, iOS, Flutter, Kotlin Multiplatform for Cross-Platform Mobile Development,
Swift, React Native for mobile, Integration of artificial intelligence systems in mobile
development, Distribution of mobile applications

Web development

Web development using Flask / Django, Authentication methods for web services, Organization of infrastructure and deployment of web services, Web analytics, Social network analysis, Crawlers, analytical platforms, Integration of artificial intelligence systems in web development, JavaScript

Software development

C, C++, Java, Go, Digital Immune System, Superapps, Platform Engineering, AI Code Generation


Internet of Things, Industrial Internet of Things, Artificial Intelligence of Things, Edge AI, Embedded AI, Autonomous Systems (drones, vehicles, etc)

AI in Automotive


AI Virtual reality

Metaverse Digital twins

Building of products empowered by AI

Usage of AI in modern services, applications, platforms

AI in COVID researches and solutions, AI in sustainable,
ecological and environmental technologies
Decision Support Systems and Applied Observability

Cyber security and AI

Soft Skills

Communication, Collaboration and teamwork, Time management and organization, Empathy / Emotional intelligence, Owning up to errors, Problem solving and creativity, People skills and management, Innovation, Analytical thinking

Opening ceremony of the school

Vitaliy Kukharskyy - Vice-Rector for Research, Teaching and IT-Development

Diana Khrushchova, AI Program Manager, Ministry of Digital Transformation of Ukraine

Nataliia Solina, CSR Lead at N-iX

Oleksandr Tkachenko, UA West and South Head of EPAM Campus at EPAM Ukraine, PhD

Andriy Pereymybida, Talent Acceleration Center Director, SoftServe University

Julia Tsymbala, Education Lead at Lviv IT Cluster

Martin Braun, CTO NeuroForge



Speakers (in process)

Aleksandra Przegalinska, Associate Professor and Vice-President of Kozminski University

Aleksandra Przegalinska  is an Associate Professor and Vice-President of Kozminski University, responsible for International Relations as well as Senior Research Associate at the Harvard Labor and Worklife Program.  Aleksandra is the head of the Human-Machine Interaction Research Center at Kozminski University, and the Leader of the AI in Management Program. Until recently, she conducted post-doctoral research at the Center for Collective Intelligence at the Massachusetts Institute of Technology in Boston. She graduated from The New School for Social Research in New York. She is the co-author of Collaborative Society (The MIT Press), and Strategizing AI in Business and Education (Cambridge University Press) published together with Dariusz Jemielniak.


David Serna Perez, Sales Director, weav.ai

David Serna Perez is a seasoned professional with over a decade of experience driving enterprise adoption of Artificial Intelligence (AI). With a keen focus on enhancing revenue, reducing costs, and mitigating risks, David has been instrumental in empowering organizations to harness the power of AI effectively. Currently serving as the Sales Director at weav.ai, David plays a pivotal role in steering the company's trajectory as an Insurance Enterprise Copilot. His expertise lies in partnering with Enterprises to craft tailored AI solutions that align seamlessly with the unique needs of each enterprise, driving transformative results.

Real-world AI outcomes from sales perspectives

Enterprise business strategies typically fall into three key categories: increasing revenue, reducing risk, and reducing cost. These strategies aim to address critical challenges and help businesses become or remain industry leaders.
For too long, we have been asking the wrong question: How can we use Generative AI (Gen AI)?
The better question is: What important business decisions can Gen AI help you make?
Today, you will discover the answers to these pressing enterprise questions and learn how they leverage Gen AI to achieve your business goals.



Nikita Kiselov, Principal Applied Scientist, Neurons-Lab

I am Nikita Kiselov, a Principal Applied Scientist at Neurons Lab. I have worked and led innovative projects in video generation, computer vision, NLP and many other areas collaborating or working in French and Canadian research institutes.

State of Video Generation: How AI Learned to be more real than Real World

This presentation explores the evolution of video generation technology, from early diffusion models to the latest advances with the power of large language models (LLMs) We’ll journey through the history of how AI learned to generate stunningly realistic videos, including advancements that now allow AI to produce videos with accompanying audio. We will discuss how completely different approaches are merging through the history and secrets of the latest models like SORA.



Yaroslav Andreiev, Middle Full Stack Developer at E-Docs

Organization of infrastructure and deployment of web services

This lecture will provide a comprehensive guide to the organization of infrastructure and the deployment of web services, using ASP.NET for application deployment as example. Additionally, the content will be aligned with the AZ-900 certification on Azure Fundamentals, offering a solid understanding of cloud concepts. Objectives: - To understand the key components and best practices for organizing infrastructure for web services. - To learn the fundamentals of deploying web applications using ASP.NET. - To grasp essential cloud concepts and services provided by Microsoft Azure. - To gain practical knowledge on creating deployment processes, including Continuous Integration and Continuous Deployment (CI/CD) pipelines. - To learn how these practices are implemented in real-world IT companies. This lecture is ideal for beginners who are new to web services deployment and cloud concepts, as well as for those looking to gain practical experience with ASP.NET and Azure. Participants will leave with the knowledge and skills to begin deploying their own web services.



Iryna Mysjuk, Senior Software Test Automation Engineer (Java),EPAM

Google Cloud Platform

During the lecture, we will explore the basic principles of GCP as a cloud environment. In addition, we will review the main capabilities and principles of GCP, the architectural structure of the developed system.

Maksym Skorupskyi, Lead Data Engineer at SoftServe, Ivan Franko National University of Lviv

Big Data in GCP

In this presentation, we'll explore how Google Cloud Platform (GCP) and a modern data stack join forces to transform your data journey. You'll discover how GCP empowers you to ingest, analyze, and unlock insights from your data, fueling smarter decisions and building a secure data foundation for the future.

Maksym Yakubovych, Engineering Manager at GlobalLogic, Ivan Franko National University of Lviv

Database basics, relational, non-relational, distributed databases

We'll run through the overview of the DB basics, ways to operate with, and main use cases. In a nutshell we will cover how relational databases organize structured data, how NoSQL databases handle unstructured data, and the benefits of distributed databases for scalability and fault tolerance.

 Ivan Bulka, Senior Data Scientist, SoftServe, Ivan Franko National University of Lviv

LLM, Transformers, BERT, GPT models family (GPT-1, 2, 3, 3.5, 4)

Advanced language models and algorithms in AI, including LLM, Transformers, BERT, and the GPT models family (GPT-1, 2, 3, 3.5, and 4). Development, utilities, and future implications of these machine learning models in natural language processing and understanding

Bohdan Buhrii, Senior Software Engineer, SoftServe, Ivan Franko National University of Lviv

LLM fine-tuning techniques

During the lecture, we will explore why you shouldn't settle for a generic LLM and how fine-tuning unlocks its true potential. We will dive into various approaches, including Parameter-Efficient Fine-Tuning (PEFT) and Retrieval-Augmented Generation (RAG), to tailor LLMs to specific tasks and domains, empowering them to excel in your unique needs.

Oleh Dutsiak, Senior Software Engineer at N-iX, Ivan Franko National University of Lviv

MS Azure

This presentation dives into Microsoft Azure's comprehensive AI platform, empowering developers to build intelligent applications at scale. We'll explore how Azure AI simplifies your AI toolchain, fostering the creation, evaluation and deployment of cutting-edge solutions.

Oleh Sihunov, Ivan Franko National University of Lviv

Amazon Web Services

In this lecture, we will delve into the fundamental concepts of AWS (Amazon Web Services) as a cloud platform. We will also examine the core functionalities and guiding principles of AWS

Yurii Tsydzenko, Ivan Franko National University of Lviv

Node.js back-end developer and DevOps

Version Control System basics: Git, Data Version Control (DVC), Data sources (Kaggle, etc.), ML Hubs (Hugging Face, etc.) At this lecture will understan fundamental of Version Control System, will discuss example of VCS platform. Additionally, we'll examine various data sources like Kaggle, which provide vast datasets for analysis and model training, and discuss how to integrate these into your projects.

Markiyan Fostiak, Ivan Franko National University of Lviv

Software Engineering Lead

Cloud computing basics, SaaS, PaaS and IaaS We will explore the fundamentals of cloud computing, focusing on its three primary service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). We will discuss how each model operates, their benefits, and use cases, providing a comprehensive understanding of how they can be leveraged to enhance business operations and technological innovation.

Mykola Stasiuk, Senior Software Engineer, GlobalLogic, Ivan Franko National University of Lviv

Machine Basic libraries: Numpy, Pandas, Scikit-learn, Seaborn, matplotlib, sktime, skforecast

In this lecture, we'll cover essential libraries for machine learning: NumPy for numerical computing, Pandas for data manipulation, Scikit-learn for machine learning algorithms, Seaborn and Matplotlib for data visualization, and sktime and skforecast for time series data.

Ihor Drozdov, Ivan Franko National University of Lviv

NLP libraries and frameworks, NLTK



Oksana Pomorova,Lead software engineer, GlobalLogic

ChatGPT usage, ChatGPT API, tokenization, creation and usage GPTs

The students will gain a comprehensive understanding of ChatGPT's functionalities, integration, and practical applications. They will explore various examples and use cases of ChatGPT, and learn about HTTP requests (GET, POST) and

Vasyl Lyashkevych, PhD, Solution Architect, GlobalLogic

AI Code Generation, Copilot


Vitalii Miroshnychenko, Lead software engineer, GlobalLogic

Machine learning basics
During this lesson, we will introduce you to basics of the artificial intelligence and how to fit models. We show what models are the best for different problems, how to build your first AI, measure its quality, and how to build the simplest analog of the ChatGPT. Common mathematical skills are required (calculus, algebra, and statistics).

Deep learning basics
In this lecture, we will introduce you to deep learning concepts and the current state of this field. Our goal is to introduce modern architectures for common problems, the development history, and the application to computer vision and

Generative models basics
Generative AI is the most interesting modern AI topic. In this lecture, we will introduce diffusion models that allow to create of production-ready images. GANs and VAE topics are added for the historical context. ML, DL skills, and common

Artem Matiash, Co-Founder, Head of Execution at Machine Factor Technologies, Principal Quantitative Researcher at Neurons Lab

Stacked classifiers for generating and validating trading signals

Intro: "M.Sc. in Systems Analysis of Financial Markets with 6+ years of hands-on experience in Algorithmic Trading across a variety of financial instruments. Worked as a quant researcher and consultant (Hudson & Thames, Neurons-Lab) to integrate ML solutions for multi-billion hedge funds, high-net-worth individuals, and start-ups. Currently launching a multimillion crypto hedge fund (Machine Factor Technologies) as a co-founder and head of execution."

Abstract: "During the demonstration, we will cover the whole trading algorithm R&D cycle including a short theoretical introduction, data aggregation and analysis, model training, meta-model training, back-testing, and results assessment. The asset of choice would be a crypto perpetual futures contract traded on OKEX".



Martin Braun, CTO NeuroForge

AI in the industry

Sebastian Lützow, Senior Big Data Developer, NeuroForge

AI in the industry

Oleksandr Smyrnov, Partner, Head of Research, Machine Factor Technologies - Algorithmic trading digital assets fund

Short intro:
Senior quantitative researcher with a data science background. Developed and productionized machine learning models in the telecommunications, maritime, and financial industries. From a data science background projects include credit scoring and customer segmentation on tabular data, vessel path optimization, and job scheduling optimization using linear programming. From the quantitative research domain - development of mean-reversion trading strategies, portfolio optimization, and futures arbitrage between exchanges.

Topic: Trading Strategies Backtesting Pitfalls

Abstract for the topic: The presentation will show how simplified hypotheses about the market and interaction with exchanges during backtesting may lead to incorrect expectations from a trading strategy's performance. The talk will include an overview of the market and how the bid-ask spread can reduce expected profits; it will cover how fitting on trades data can lead to false mean-reversion discovery; why liquidity constraints are important and how to estimate your market impact.

Oleh Sunnikov, Mobile competency manager at ELEKS

Retrieval Augmented Generation in the New Processing

How to combine the relevant information with generative models to produce accurate, contextually rich, and up-to-date content w/o the need to rebuild or finetune a model.

Anastasiia Tolkachova, Senior Security Tester Engineer

Security vulnerabilities in Large Language Model Applications

Penetration testing. Ethical hacking. Examples of vulnerabilities include prompt injections, data leakage, inadequate sandboxing, and unauthorized code execution, among others.

Josh Pratt, CEO & Founder/Sales Enthusiast

ChatGPT, customGPT, and its Rightful Place

Understanding how to use AI depending on your specific situation can lead to either great success, or great failure. We'll be looking at the Dos and Dont's of AI implementation into your workforce

Roman Babyuk. Devops Lead, Adorama.com

Security and Secrets Data in Modern Applications

- what is secret data in code
- what can we generally do to do not store the data in code
- if we have to how can we encrypt
- what are pitfalls
- secrets in cloud environments
- best practice

Tsemko Andrii, Senior Engineer, Infineon Technologies AG

Embedded AI: From Training to Deployment of Neural Networks on Edge Devices

This presentation shows the workflow of developing neural network models for microcontrollers, from PC training to deployment on edge devices. Uncover how Embedded AI works, its challenges, and solutions. Key features of Embedded AI solutions will be discussed, with a demonstration of tools like TensorFlow Lite and Infineon’s ML Configurator and ML Middleware for model conversion and deployment on microcontrollers.

Rolan Akhmedov, Technical Lead of Computer Vision Department

CV Libraries and frameworks, OpenCV

1. OpenCV
2. MediaPipe
3. TensorFlow vs PyTorch
4. STL + ctype
5. SKLearn
6. And more over..

Artificial Intelligence of Things, Embedded AI, AI Autonomous Systems (drones, vehicles, etc)

Viktor Ladyzhets, Senior Software Engineer at N-ix

Recommender Systems: An overview

- Inctroduction: what is a recommendation system and what makes a good one
- Types of recommender systems: Collaborative filtering, content-based and hybrid
- Algorithms and models used for building recommender systems
- Evaluation metrics
- Common challenges and problems
- Data: where to get and how to prepare
- Practical apllications: where to start and how to build one
- Q&A

Vyacheslav Koldovsky, Competence Manager, SoftServe

GenAI for JavaScript-developers



Halyna Dychko, Data Scientist at N-iX

Responsible AI Overview. Fairness issues in credit-card default models

This lecture will begin with an introduction to the concept of Responsible AI, exploring its definitions and components. We will then delve into the topic of fairness in machine learning, unpacking the types of unfairness and ways to mitigate them. Additionally, we’ll examine how fairness issues manifest in a close to real-world applications, specifically focusing on the credit card default prediction task.

Oleh Shyshkin, Tech Lead (Big Data Engineering)

 Prototyping your AI product

Where to start? What do you need to remember when starting your project? How do you keep it organized and well-structured? We will do live prototyping for a toy AI product in the workshop. After that, you will know:
- what you can use for rapid development
- how to build your project so it is easy to test and maintain
- how to support multi-versioning and keep track of product changes



Andriy Zhyshkovych, Full Stack Software Engineer at EPAM Systems, Ivan Franko National University of Lviv

Front-End Essentials: Building Interactive Interfaces

"In this session, we will explore the fundamentals of front-end development, focusing on HTML, CSS, and JavaScript. You will learn how to create and style web pages, add interactivity, and briefly touch on the purpose and importance of modern front-end frameworks. By the end of the session, you'll be equipped with the essential skills to build engaging and interactive user interfaces."

Back-End Essentials: The Engine Behind the Web

"This session will introduce you to the core concepts of back-end development, including server-side programming, databases, and APIs. We will cover the basics of server technologies, how to interact with databases, and the use of APIs. By the end of the session, you'll have a solid understanding of how to power web applications from behind the scenes."

Igor Kolych, Ivan Franko National University of Lviv

Supervised Machine Learning.

The session introduces supervised machine learning algorithms such as linear and logistic regressions and more. We will consider in detail the optimization methods, data normalization, and regularization. The lecture is a good start for future learning and is considered interactive.



Volodymyr Anokhin, Ivan Franko National University of Lviv

Python for data analysis.



Oleksandr Haliatkin, Software Engineer at LLC "Business and Technologies", Ivan Franko National University of Lviv

Popular NoSQL Databases for Web Development. A Practical Approach

In this lecture, we will discuss NoSQL databases, specifically comparing MongoDB and Firebase. You will learn about which database is used for efficiently storing large volumes of data and handling complex queries, and which one serves as a full-fledged real-time platform that not only includes a database but also provides various tools for application development, such as user authentication systems, analytics tools, and real-time data synchronization mechanisms. In addition, database creation and practical applications will be considered.

Oleh Gusak, Ivan Franko National University of Lviv

NoSQL: Key-Value, Column-based, Document-based, Graph databases





General partner

Official partners



Information partners



How to join?

1) To provide the school with a speaker or mentor;

2) to support the project implementation by becoming its partner.

Advantages of partnership:

  • additional promotion of your company among students;
  • communication and involvement of the students into the company's activities;
  • distribution of information content and posts about the company in the social networks of the University;

If you want to take part in schoolwork or cooperate with us, send us an email: marketing@remove-this.lnu.edu.ua