DESCRIPTION
This short course offers a good overview to all current progress in Deep Learning. It is ideal for persons (scientists, engineers, students, AI enthusiasts) interested in AI upskilling or reskilling. The only background knowledge needed is Mathematics (Calculus, Linear Algebra, Probability Theory) that are included in any Science or Engineering Curriculum. Persons coming from other backgrounds, e.g., Medicine or Linguistics, can also benefit, if they have some mathematical knowledge.
The course starts with an introduction to Machine Learning, namely clustering, classification, regression and their applications. More advanced topics, e.g., federated learning, continual learning, knowledge distillation are also overviewed. Then, Artificial Neural Networks (ANNs) are presented, in particular Perceptron, Multilayer Perceptron and their training based on Backpropagation. State-of-the-Art Deep Neural Networks (DNNs) are detailed, including Convolutional Neural Networks (CNNs) and Attention and Transformers Networks. Finally, all aspects of Generative AI are overviewed, namely the Large Language Models (e.g., ChatGPT), Generative Adversarial Networks and Diffusion Models used in Multimedia Creation.
While this short course is scheduled in connection with the AIDA Symposium and Summer School on ‘AI/ML Cutting Edge Trends’ (AIDA AICET2025), it is not merely a part of the symposium. Participants can attend this course independently, without the obligation to enroll in the full AIDA Symposium and Summer School.
LECTURES
- Introduction to Machine Learning
- Artificial Neural Networks. Perceptron
- Multilayer Perceptron. Backpropagation.
- Convolutional Neural Networks
- Attention and Transformers Networks
- Large Language Models
- Generative Adversarial Networks in Multimedia Creation
- Generative AI and Diffusion Models
Figure 1: CNN architecture.
Figure 2: Typical Transformer architecture.
Figure 3: Image repainting.
WHEN?
The course will take place on Sunday, 13 July 2025.
WHERE?
This course is hybrid (local/remote participation).
Local participation: KEDEA building, Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece.
Remote participation: Zoom link will be available in due time.
You can find additional information about the city of Thessaloniki and details on how to get to the city here.
REGISTRATION (to be opened soon)
| LOCAL | REMOTE | |
| Full | 190 EUR | 150 EUR |
| Students | 140 EUR | 90 EUR |
| AIDA Students | 120 EUR | 80 EUR |
REGISTRATION CATEGORIES
Full Registration
Student Registration:
- 30% discount for Unemployed or Undergraduate/MSc/PhD student.
- Requires proof of student status (valid student ID or official university letter).
AIDA Student Registration:
- 40% discount for AIDA students (PhD students/candidates or Postdoc researchers belonging to any AIDA member).
- Requires proof of AIDA student status (valid AIDA student certificate confirmation by an AIDA lecturer or AIDA university representative in accordance with AIDA policy – checked by AIDA, there is no need to send any document).
A certificate of attendance will be provided by AUTH upon successful completion of the course.
For the successful completion of the course, the participants should:
Α) have attended more than 70% of the lectures.
B) have paid the tuition fee by 11/07/2025.
LECTURER
Prof. Ioannis Pitas
Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and since 1999 Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.
His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 970 papers, contributed to 48 books in his areas of interest and edited or (co-)authored another 16 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 23 international journals and General or Technical Chair of 5 international conferences. He delivered 151 keynote/invited speeches worldwide. He co-organized 38 conferences and participated in technical committees of 291 conferences. He participated in 75+ R&D projects, primarily funded by the European Union and is/was principal investigator in 47 such projects. He is the coordinator of the Horizon Europe R&D project TEMA ( https://tema-project.eu/) , AUTH principal investigator in Horizon Europe R&D projects AI4Europe (https://www.egi.eu/project/ai4europe/) and SIMAR (https://simar-project.eu/). He is chair of the International AI Doctoral Academy (AIDA) https://www.i-aida.org/. He was chair and initiator of the IEEE Autonomous Systems Initiative https://ieeeasi.signalprocessingsociety.org/. He has 38300+ citations to his work and h-index 92. According to https://research.com/ he is ranked first in Greece and 319 worldwide in the field of Computer Science (2022).
Educational record of Prof. Ioannis Pitas
Prof I. Pitas was Visiting/Adjunct/Honorary Professor/Researcher and lectured at several Universities: University of Toronto (Canada), University of British Columbia (Canada), EPFL (Switzerland), Chinese Academy of Sciences (China), University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany), National University of Malaysia, Henan University (China). He delivered 129 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He ran 17 short courses and tutorials on Autonomous Systems, Computer Vision and Machine Learning, most of them in the past 3 years in many countries, e.g., USA, UK, Italy, Finland, Greece, Australia, N. Zealand, Korea, Taiwan, Sri Lanka, Bhutan.
USEFUL LINKS
Prof. Ioannis Pitas: https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el
https://research.com/university-rankings/computer-science/gr