Poznan University of Technology
AI Summer
School
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.
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!
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 deep learning techniques such as recurrent neural networks (RNNs) and transformers, which have led to significant advancements in the field of NLP. 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. Although prior knowledge of AI and NLP is beneficial, it is not required to attend the lecture.
Low-code / no-code tools for Artificial Intelligence
The laboratory for Low-Code No-Code (LCNC) 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 a variety of user-friendly tools that are intuitive and require minimal coding. These tools are designed to assist researchers in performing essential data science tasks such as data cleaning, visualization, and analysis. With LCNC 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 advanced programming knowledge. The LCNC 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. 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 held prominent positions at renowned institutions, published extensively in reputable journals, and received numerous accolades for his groundbreaking work. His research combines theoretical foundations with practical applications, making him a sought-after expert in the field.Â
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. Jerzy Stefanowski
is a highly acclaimed academic and researcher in the field of computer science, specializing in data mining, machine learning, and artificial intelligence. With a career spanning over three decades, he has made substantial contributions to knowledge and technology advancement in these areas.
Prof. Stefanowski has held prestigious positions at renowned institutions, both nationally and internationally, and is recognized as a leading authority in the field. His research focuses on the statistical learnig theory, explainability of AI models and ethical & trustworthy AI.
Prof. Stefanowski will present the overview of the contemporary AI, debunk several myths and misconceptions surrounding artificial intelligence, and discuss the applicability and usefulnes of AI in various scientific disciplines.
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 tools for low-code and no-code AI, where everyone can use AI techniques without the need to code.
Register
Schedule of our program
01
4th September
| 8:45-9:00 | Welcome & organization briefing |
| 9:00-10:30 | Keynote lecture by prof. W. Samek |
| 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 (case study) |
02
5th September
| 9:00-10:30 | Introduction to Artificial Intelligence, prof. J. Stefanowski |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Introduction to Artificial Intelligence, prof. J. Stefanowski |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Workshop on AI-related challenges in scientific research |
03
6th 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 |
Our location
04
7th 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
8th September
| 09:00-10:30 | Laboratory on no-code/low-code tools, prof. M. Morzy |
| 10:30-11:00 | Coffee break |
| 11:00-12:30 | Laboratory on no-code/low-code tools, prof. M. Morzy |
| 12:30-14:00 | Lunch break |
| 14:00-15:30 | Laboratory on no-code/low-code tools, prof. M. Morzy |
| 15:30-16:00 | Summer school closing |