Poznan University of Technology
AI Summer
School
2-6 September 2024
Summer School on Applied and Interdisciplinary Artificial Intelligence
Idea
Our summer school is aimed at doctoral students conducting research in all scientific disciplines (except computer science!). We especially welcome those representing the humanities, social sciences, life sciences and artistic fields.
Our main goal is to de-mythologize artificial intelligence and present, in a clear, simple and accessible way, what tools and methods are available today. We want to show you how AI and machine learning can help you in your research.
What to expect
Artificial intelligence (AI) has revolutionized the way scientific research is conducted across various disciplines, from biology to physics, and from chemistry to astronomy. It has become an essential tool for researchers to analyze complex datasets, recognize patterns, and make predictions. AI has the potential to accelerate the pace of scientific discovery by automating data collection and analysis, reducing human errors, and unlocking hidden insights from vast amounts of data.
How is it possible that our Summer School is not only free of charge, but we’ll also pay for your hotels and half-board alimentation? It is simple: the Summer School (S2AI2) is financed from the funds of the INPUTDoc Project received by the Doctoral School of the Poznan University of Technology, from the Polish National Agency for Academic Exchange (NAWA) under the STER Programme “Internationalisation of doctoral schools”.
This way, we can offer you a place in a hotel and provide you with coffee breaks and lunches. The only thing we cannot cover is the cost of your travel to Poznan, unfortunately. But remember that Poznan is easily accessible from across Europe, so that should not be a problem.
Our summer school is aimed at doctoral students conducting research in all scientific disciplines. We especially welcome those representing the humanities, social sciences, life sciences and artistic fields.
Our main goal is to de-mythologize artificial intelligence and present, in a clear, simple and accessible way, what tools and methods are available today. We want to show you how AI and machine learning can help you in your research.
In order to be eligible for participation, you have to be either enrolled in the Doctoral School of Poznan University of Technology, or be enrolled in any doctoral program at a university outside of Poland. We regret but due to the rules of the NAWA funding we cannot accept doctoral students from other Polish universities.
Have you never programmed? Fantastic, this summer school is just for you. You don’t need to have any experience with artificial intelligence to understand the possibilities it brings today and how you can use it.
Of course, we won’t teach you how to program! But you will understand exactly what possibilities for analyzing images, text, and data modern artificial intelligence brings and how you can take advantage of these possibilities in your research. Boost your academic career with
Artificial Intelligence now!
Design Thinking workshop
The goal of the Design Thinking Jam workshop is to integrate participants
of the summer school and familiarize them with the design thinking
methodology in the context of conducting innovative scientific projects.
The workshop will be conducted by Design Thinking Institute. We will go
through all stages of the method, from empathy to testing. You will
learn about a dozen tools and principles of work during each stage. We
will finish with several solutions after the first, quick tests.
Design thinking is a creative method of solving problems that is based on
openness, continuous improvement, learning from others and understanding
the needs of users. It is an approach focused on innovation and
teamwork.
Subjects
Introduction to AI
Welcome to our lecture on “Introduction to Artificial Intelligence” aimed at young researchers who are not from a computer science or ICT background. In this lecture, we will provide a comprehensive overview of AI and its applications in various fields. We will start by discussing the basic concepts of AI, such as machine learning, deep learning, and neural networks. We will also explore the different types of AI, including narrow or weak AI and general or strong AI. Our goal is to help you understand the potential of AI in various domains such as healthcare, finance, and education. We will also discuss the ethical implications of AI and its impact on society. By the end of this lecture, we hope to equip you with the foundational knowledge to understand and engage with the exciting and rapidly evolving field of AI. No prior knowledge of computer science or ICT is required, just a curious mind and a desire to learn about this fascinating field.
Artificial Intelligence for Computer Vision
This lecture is aimed at researchers who are interested in exploring the potential of AI in image processing, interpretation, and computer vision. We will introduce the essntial conceptual framework allowing application of AI methods, and in particular machine learning (ML), in these domains. The usage scenarios of particular interest will include, among others, object/image classification, image segmentation and visual reasoning. A fair share of this lecture will concern convolutional neural networks (CNNs) and other deep learning architectures that proved very successful at processing imaging data, like autoencoders, residual architectures, generative adversarial networks, transformers, and, time permitting, neurosymbolic systems. Preliminary knowledge of image processing and ML is recommended.
Artificial Intelligence for Text Processing
This lecture on “Artificial Intelligence for Text Processing” is designed for individuals who are interested in exploring the uses of AI in text processing. The lecture will cover the basic principles of AI and how it can be applied in natural language processing (NLP) to analyze and comprehend text, extract important information, and generate responses that are similar to human-like responses. The lecture will also introduce the concept of text embeddings, which have led to significant advancements in the field of NLP. The second part of the lecture will focus on generative AI, the explanation of how large language models (LLMs) such as GPT or LLaMa work, and how these models can be used to super-charge scientific work in all disciplines. The goal of the lecture is to provide a comprehensive understanding of how AI can be utilized to process and understand textual data and improve its overall quality. By the end of the lecture, attendees will have a solid grasp of the latest advancements in AI for text processing and how they can be implemented in their research or professional work.Â
No-code tools for Artificial Intelligence
The laboratory for No-Code (NC) tools is designed to provide researchers from non-computer science backgrounds with the ability to perform data science tasks without requiring extensive programming knowledge. The lab includes tutorials with user-friendly tools that are intuitive and require no coding. These tools are designed to assist researchers in performing essential data science tasks such as data cleaning, visualization, and analysis. With NC tools, researchers can easily and quickly create interactive dashboards, perform machine learning tasks, and generate reports. The lab provides an opportunity for researchers from different disciplines to explore and analyze data without the need for any programming knowledge. The NC lab is a valuable resource for researchers who want to leverage technology to enhance their research without investing significant time and effort learning how to code.
prof. Vir V. Phoha
Professor’s Phoha primary interest lies in security, including areas such as malignant systems and active authentication, illustrated by touch-based authentication on mobile devices. Additionally, his research encompasses machine learning methodologies like decision trees, statistical approaches, and evolutionary methods, emphasizing the analysis of extensive time series data streams and static data sets. Prof. Phoha is also dedicated to exploring computer networks, particularly in detecting anomalies and optimizing network performance. Furthermore, they apply these techniques to develop practical, field-ready defensive and offensive cyber-based systems. His invited lecture will focus on the introduction to cyber-security in the age of AI.
prof. Krzysztof Krawiec
Distinguished academic and researcher specializing in the field of evolutionary computation. With a focus on genetic programming and machine learning, he has made significant contributions to the advancement of computational intelligence.Â
Prof. Krawiec has published extensively in scientific journals and conferences, receiving numerous accolades for his work. His research combines theoretical foundations with practical applications. Given his experiences with using AI in medical imaging, prof. Krawiec will introduce AI for images: how does it work, what can be extracted from images, what models are available, how AI for images can help boost research.
prof. Dariusz Brzeziński
.Associate Professor at the Institute of Computing Science, Poznan University of Technology. He received his Ph.D. and D.Sc. in computer science from Poznan University of Technology, Poland, in 2015 and 2019. He also spent some time at the Department of Molecular Physiology and Biological Physics at the University of Virginia. His research interests include machine learning, structural biology, and algorithmic fairness. He also dabbled in projects involving genome-based diagnostics, flow cytometry, material science, product demand prediction, data stream mining, and AI in computer games.
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prof. Mikołaj Morzy
is the deputy dean for science at the Faculty of Computingand Telecommunications at PUT. His research interests focus on machine learningand its applications in natural language processing, complex network systems, and social networks.
He has helped organize several editions of the Machine Learning Meetup in Poznan, a vivid community of over 600 machine learning practitioners and enthusiasts. He is also active in the field of science popularization.
In recent years he has been invited to give lectures on machine learning during three TEDx events in Poznan and Bydgoszcz. During the Summer School he will present the lecture on Artificial Intelligence for Text and he will lead the lab on no-code tools forAI, where everyone can use AI techniques without the need to code.
Register
Registration continues until August 18, 2024
Schedule of our program
01
2nd September
| 8:45-9:00 | Welcome & organization briefing |
| 9:00-10:30 | Keynote lecture by prof. Vir V. Phoha |
| 10:30-11:00 | Coffee break |
| 11:00-14:00 | Design thinking workshop |
| 14:00-15:00 | Lunch break |
| 15:00-18:00 | Design thinking workshop |
02
3rd September
| 9:00-10:30 | Introduction to Artificial Intelligence, prof. D. Brzeziński |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Introduction to Artificial Intelligence, prof. D. Brzeziński |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Workshop on AI-related challenges in scientific research |
03
4th September
| 09:00-10:30 | Artificial Intelligence for Computer Vision, prof. K. Krawiec |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Artificial Intelligence for Computer Vision, prof. K. Krawiec |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Workshop on using AI for processing image data |
04
5th September
| 09:00-10:30 | Artificial Intelligence for Text, prof. M. Morzy |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Artificial Intelligence for Text, prof. M. Morzy |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Workshop on using AI for processing text data |
05
6th September
| 09:00-10:30 | Laboratory on no-code tools, prof. M. Morzy |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Laboratory on no-code tools, prof. M. Morzy |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Laboratory on no-code tools, prof. M. Morzy |
| 15:30-16:00 | Summer school closing |