The Center for Education and Lifelong Learning of the Aristotle University of Thessaloniki welcomes you to the “CVML Short Course – Machine Learning and Deep Neural Networks”, a 16-hour online course via zoom application.
The Director of the Programme is Ioannis Pitas, Professor, School of Informatics, AUTh.
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 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 1000 papers, contributed in 47 books in his areas of interest and edited or (co-)authored another 11 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 9 international journals and General or Technical Chair of 4 international conferences. He participated in 70 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 42 such projects. Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/. He is AUTH principal investigator in H2020 R&D projects Aerial Core and AI4Media. He is chair of the Autonomous Systems Initiative https://ieeeasi.signalprocessingsociety.org/. He is head of the EC funded AI doctoral school of Horizon2020 EU funded R&D project AI4Media (1 of the 4 in Europe). He has 32200+ citations to his work and h-index 85+ (Google Scholar).
Start Date: 17/02/2021
End of the course: 18/02/2021
Duration: 16 hours
The participants will receive a Certificate of attendance.
Applications are submitted online from 5/01/2021 to 16/02/2021
Aim and objectives
The aim of this course is to provide useful knowledge on topics related to “Machine Learning and Deep Neural Networks” to young scientists.
Participant selection & Requirements
Priority order will be observed based on filing date up to 40 people.
The course aims at young professionals and academics.
Ø Mathematical background
Ø Internet access
Introduction to Machine Learning, Artificial Neural Networks, Perceptron, Multilayer perceptron, Backpropagation, Deep neural networks, Convolutional NNs, Deep learning for object detection, Deep Semantic Image Segmentation, Generative Adversarial Networks, Recurrent Neural Networks, Data Clustering, Decision surfaces, Distance based classification, Dimensionality reduction, Kernel methods, Bayesian learning, Deep Reinforcement Learning and CVML programming tools.
Ø PDF slides will be available to course attendees
Ø Lectures will be prerecorded to facilitate attendees in case they experience problems due to time difference.
There will not be.
Upon completion of the course, participants will be awarded a Certificate of attendance.
For the successful completion of the programme, the participants should:
A) have attended all the teaching units. Absences may not exceed 10% of the scheduled training hours.
B) to have paid all the tuition fees by 16/02/2021.
Early registration (till 8/02/2021): 200€ students, 300€ standard
Registration (after 8/02/2021): 250€ students, 350€ standard
Up to 10 PhD students, registered in AUTH or in any VISION CSA https://www.vision4ai.eu or AI4Media https://ai4media.eu/ University partners, are entitled for free course registration on a FCFS basis, with priority to ones working on AI-related topics. This offer is related to the upcoming educational activities of International AI Doctoral Academy (AIDA) http://126.96.36.199/wordpress/ that is co-initiated by these two projects.
For further information, please contact with Mrs. Koroni Ioanna at firstname.lastname@example.org.