Volodymyr Melnyk — Rector at Ivan Franko National University of Lviv

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

Volodimir Sitnіkov, CEO Brainstack_

Zavgorodnii Dmytro, Deputy Minister of Education and Science of Ukraine for digital development, digital transformations and digitalization

Oleksandr Bornyakov, Deputy Minister of Digital Transformation for IT Development

Denys Grynov, Head of EРАМ University in Ukraine, Vice President on Educational issues of the IT Ukraine Association

Igor Kostiv, Associate Vice President, Engineering, Global Logic

Sviatoslav Kavetskyi, Director of Industry Affairs at N-iX

Julia Tsymbala, Education Lead at Lviv IT Cluster

Ivan Korzhov, Country Manager at Infopulse

Martin Braun, Co-Founder at NeuroForge

Galyna Dychko, Middle Data Science: Generic, N-iX

The pitfalls of trying to extract causal insights from a predictive machine learning model

Have you ever used an interpretability tool (e.g. SHAP) to answer causal questions? Were you surprised by strange patterns like the more users report bugs the more likely they are to renew or users with significant discounts are less likely to continue a subscription? Let’s figure out together when SHAP fails to answer what-if questions and when it brings real values for causal inference.

Oleksandra Derevytska, Middle Test Engineer, ELEKS

Humanistic Approach to Time Management

- who where why: why context matters for planning
- why some people are not able to use common planning approaches
- what hints can be used
- overview of popular methods

Iurii Bregman, Senior Software Developer, ELEKS

S.O.L.I.D.  and other practices

- Explaining why it's important to prioritise quality over speed in professional work

-Explaining each of the SOLID principles one by one-The KISS principle and it's importance

-Propaganda of Refactoring -Propaganda of TDD

-Propaganda of Pair Programming   

Bohdan Trynka, Middle Software Developer, ELEKS

Introduction:  

Briefly introduce the concept of Artificial Intelligence (AI) and its impact on various industries. Highlight the significance of AI in web development, revolutionizing user experiences and interactions.    

Thesis 1: Training AI Models for Web Development

Thesis 2: Empowering Web Applications with ChatGPT

Thesis 3: Enhancing User Experience with AI-Driven Personalization

Thesis 4: Improving Web Accessibility with AI

Thesis 5: AI-Driven Content Generation and Curation

Thesis 6: AI-Powered Security and Fraud Detection

Thesis 7: Streamlining Web Development with AI Tools and Frameworks

Thesis 8: Ethical Considerations in AI-Driven Web Development

 Thesis 9: The problem of dependence on the provider 

Thesis 10: Preserving the context when working with the API and tuning

Vitalii Miroshnychenko, PhD in statistics, Lead software engineer, GlobalLogic

1. ML Toolchain
Some things an engineer should not write at all, because thousands of people around the world worked on it for him. In this lecture, I will talk about the main libraries used by ML and Data science engineers to solve project problems: numpy, pandas, tensorflow and others. With the help of these libraries, we will demonstrate both simple examples (for example, how to work with data sets) and complex ones:
- data preparation,
- acceleration of calculations,
- and how easy it is to transfer calculations from the processor to graphics cards

2. Deep learning
Usually, the work of an ML engineer consists of creating or using deep neural network architectures. For some tasks, it will not be possible to use ready-made models, so you have to write them yourself. In this lecture, we will consider the main modern approaches and problems that are solved with the help of deep learning:
- Layers of neural networks
- Model architectures for various tasks
- Construction of own neural network
- Learning generative models

3. Deep learning for forecasting
Most of the problems can now be solved by already created architectures of deep neural networks, but there are many problems that are difficult to solve with an architecture "out of the box" due to the specificity of the data. Examples of such tasks are various search engines and recommendation algorithms. Usually, such problems are solved not by one model, but by a set of models. In this lecture we will consider:
- Simple regression examples
- Neural network architectures that transform pictures (or text) into a short vector of numbers
- Multimodal architectures (Working simultaneously with images and text)
- Some approaches to building product recommendation systems or search engines

Kulyk Petro Ruslanovych, Java, Kotlin developer @ IFNUL

Assistant of the System Design Department, Faculty of Electronics and Computer Technology ,

The range of interests includes non-relational databases, Big Data, personal knowledge system and neural networks. I have been working on development and maintenance of Public Domain Software for desktop personal computers for four years.

Marta Balahurak, Senior Android Developer, Kindgeek Mobile Development, Android

Android basics. Android development roadmap

Interested in building Android apps but not sure where to start? Or maybe you’re still exploring different technologies?  Join me at my Android lecture where we’ll dive into the basics of Android development and uncover the roadmap to take you from beginner to pro. And don’t worry, we’ll also get hands-on and build your very first Android app during the session. Get ready for an awesome Android adventure!

Faryna Marian,Principal Software Developer, Sigma Software

Big Data: Big Data in AWS, Big Data in GCP, Big Data in Azure. Big Data Visualization.

Cloud providers went through a long path of designing, developing, and transforming their services to become a solid foundation for Big Data solutions. Each of the popular provider has its own set of services that can be used to build a modern Data Platform. Most of the services are mirrored between cloud providers, but if look into details, you will definitely find differences, and even opposite approach to solve same problems.

Igor Sharmansky, Data Scientist at Boosta, Boosta

Python

Python basics

Python for data analysis

Python for visualization

Python for data mining

Volodymyr Tkachuk, Professor, D. Sc, Chairman of the Department for Theoretical Physics, Ivan Franko National University of Lviv

Lecture "From classical to quantum bit"

The elementary cell of classical computers is a bit. It is a physical system that can be in two stable states associated with 0 or 1. In the quantum case instead of  bit, we have a quantum bit (qubit) which lives in the two-dimensional linear space spanned by two basic vectors |0> and |1>  which, similarly to  the classical case, are associated with 0 and  1. Contrary to the classical case, a quantum bit can be in a state which is a superposition of two possibilities. Note that understanding of quantum  programming is impossible without knowing quantum mechanics. Using as example a quantum bit we will present postulates of quantum mechanics. One-qubit gates will be demonstrated.

Practical course on  topic "From classical to quantum bit".

Khrystyna Gnatenko, Professor, D. Sc, Ivan Franko National University of Lviv, Department for Theoretical Physics

Lecture  "Multi-qubit quantum states and their preparation on a quantum computer"

Multi-qubit quantum states will be considered. Basic operations with the states on IBM's quantum computer will be demonstrated in detail. It is worth stressing that the entanglement plays a critical role in quantum information.  The measure of entanglement of quantum states known as concurrence will be discussed and calculated.

Practical course on  topic  "Multi-qubit quantum states and their preparation on a quantum computer".

Vasyl Davydko, Managing Partner, Leopolis Group

Local LLM (Large Language model) deployment

What is LLM
How LLM works
What are the challenges of using third-party LLM
How to deploy LLM on your local servers
Do you really need LLM

Vladyslav Kidyba, Senior analyst, Leopolis Group

Local LLM (Large Language model) deployment

What is LLM
How LLM works
What are the challenges of using third-party LLM
How to deploy LLM on your local servers
Do you really need LLM

Volodymyr Liashenko, Business Analyst, Leopolis Group

Prompting ChatGPT: how to optimize your studying.

Understanding Chat GPT
Impact on Daily Life
Practical Applications
Responsible Usage

Anhelina Zinchenko, Junior Business Analyst, Leopolis Group

Prompting ChatGPT: how to optimize your studying

Understanding Chat GPT
Impact on Daily Life
Practical Applications
Responsible Usage

Vadym Popov, Expert IT Engineer (Cloud Solutions), Infopulse

1. Cloud Concepts

This lecture provides an introduction to the fundamental concepts of cloud computing.

• IaaS (Infrastructure as a Service)
• PaaS (Platform as a Service)
• SaaS (Software as a Service)

2. Introduction to Azure Cloud Services

In this lecture, you will gain foundational knowledge of core Azure services.

• Identity
• Networking
• Storage
• Virtual Machines
• PaaS Compute Options
• Database Services
• Other Services
• Azure for Students

3. Industry Cloud Platforms

This lecture focuses on Azure services tailored for businesses in various industries.

• Azure IoT (Internet of Things) service
• General Security

Oleksii Ivanov, Expert IT Engineer (Cloud Solutions), Infopulse

1. Cloud Concepts

This lecture provides an introduction to the fundamental concepts of cloud computing.

• IaaS (Infrastructure as a Service)
• PaaS (Platform as a Service)
• SaaS (Software as a Service)

2. Introduction to Azure Cloud Services

In this lecture, you will gain foundational knowledge of core Azure services.

• Identity
• Networking
• Storage
• Virtual Machines
• PaaS Compute Options
• Database Services
• Other Services
• Azure for Students

3. Industry Cloud Platforms

This lecture focuses on Azure services tailored for businesses in various industries.

• Azure IoT (Internet of Things) service
• General Security

Maksym Druchok, LifeScience R&D Lead at SoftServe Inc., PhD in theoretical physics

"Machine learning methods in LifeScience"

During the lecture we will dive into chem- and bioinformatics concepts, will learn how to encode organic molecules, feed them to machine learning models, and predict properties of interest. Additionally, we will visit the world of precision medicines to understand how personal therapies are designed and how drug dosage or treatment prescriptions are adjusted for individual patients.

Chernjavskyj Mykola, Project manager, Indeema Software

Project Management in IoT

- What is IoT?
- The Role of Project Management in IoT Projects
- Project Planning and Risk Management
- Skills for Project Managers qualification

Volodymyr Chernetskyi, Senior DevOps (AWS) Engineer, Intellias

Cloud computing: Amazon Web Services

Discover the pre-cloud era and the challenges faced in managing data and resources. Explore various cloud computing deployment models. Uncover the advantages of cloud computing. Delve into cloud computing models, offering powerful computing resources, development environments, and ready-to-use applications. Learn about AWS, its history, extensive service offerings, and its impact on cloud adoption. Lastly, explore the exciting realm of AI and ML services on AWS, empowering data scientists to build, train, and deploy sophisticated models and extract insights from vast datasets. Take advantage of this opportunity to embark on a transformative journey in cloud computing and AWS.

What did the world look like before cloud computing?
Cloud computing deployment models.
Advantages of cloud computing.
Cloud computing models.
Amazon Web Services.
Machine Learning and Artificial Intelligence services on AWS.
How to get started with AWS?

Andrii Stepko, Senior GoLang Developer, Intellias

Go programming language

How do we use Go.
Go philosophy and specifics - what differs it from other languages.
Go routines to parallel applications.
What people usually love and dislike (rarely) in Go.
What to start with in Go.

Andy Bosyi MindCraft.ai, CEO/Founder with the presentation

Neural networks. Advanced Backpropagation Algorithm

Yurii Yevstafiiev, Software Engineering Manager, Head of JS Unit EPAM UA South

Frontend. How to start & why it is important + AI

Marian Fediv, Lead Software Engineer, Cross-location BigData Lab Leader, EPAM

Big Data - Introduction. Review. Growth

Yevhen Leshchynskyy, Senior Performance Analyst, Performance testing lab leader at EPAM WEST

Performance Testing.  The path with bricks and candies

Liubomyr Drevych, Middle iOS Swift Developer, Intellias

Enhancing Game Board Experiences on iOS: Exploring Vision-based Techniques for Recognition and Placement Using CoreML and Vision Framework

Recognizing the game board and converting it into an electronic version using Vision.
Additional shape recognition for correct placement using CoreML and Vision framework.

Marta Maloid-Hliebova, Vice-dean of faculty of Mechanics and Mathematics, Ivan Franko National University of L’viv

Oleh Buhrii, Doctor of Science, Professor, Head of Department of Mathematical Statistics and Differential Equations, Faculty of Mechanics and Mathematics, Ivan Franko National University of Lviv

Statistical Data Analysis

Valeriia Bieliavtseva, Recruiter, Boosta

First Interview: Tips & Tricks

First CV. First Interview. First Job

Oliver Fehmel, Project Manager, groupXS

Why AI is not the solution for everything (and how do software projects start at all)?

Stefan Diersch, Business Development Manager, groupXS

Why AI is not the solution for everything (and how do software projects start at all)?

Nicolai Sticht, Senior IT Strategist and Software Engineer, groupXS

Industry 5.0: Why AI is not the solution for everything (and how do software projects start at all)? 100% remote working: Why I would never want to work differently, and why it's still not always rainbows and unicorns?

Martin Braun, CTO NeuroForge

Data Science and AI Learnings from/for the Real World
Orchestrating Machine Learning Trainings and Software with MLOps & DevOps

Sebastian Lützow, Senior Big Data Developer, NeuroForge

Data Science and AI Learnings from/for the Real World
Orchestrating Machine Learning Trainings and Software with MLOps & DevOps

Artem Shelamanov, Data Scientist, NeuroForge

Data Science and AI Learnings from/for the Real World
Orchestrating Machine Learning Trainings and Software with MLOps & DevOps