CVML live Web lectures 20th May 2020: 1) Motion estimation, 2) Introduction to Machine Learning. Change of CVML web lectures date/time.

Εκ μέρους του καθηγ. κ. Ι. Πήτα σας ενημερώνουμε για το παρακάτω:

Dear Computer Vision/Machine Learning/Autonomous Systems students, engineers, scientists and enthusiasts,

Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to have launched the live CVML Web lecture series

that covers very important Computer Vision/Machine Learning topics. Two new upcoming 45 min lectures will take place soon:

1) Motion estimation

2) Introduction to Machine Learning

New!: Date/time: Wednesday 20th May 2020,  17:00-18:30 EEST for both lectures (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time).

Registration  can be done using the link:

Registration for asynchronous access to CVML live Web lecture material (video, pdf/ppt) for any past/present lecture can be done using the link:

Lecture abstracts

1) Motion estimation, Wednesday 20th May 2020, 17:00-17:45 EEST
Summary: Motion estimation principals will be analyzed. Initiating form 2D and 3D motion models, displacement estimation as well as quality metrics for motion estimation will subsequently be detailed. One of the basic motion estimation techniques, namely block matching, will also be presented, along with three alternative, faster methods. A good overview of deep neural notion estimation will be presented. Phase correlation will be described, next followed by optical flow equation methods. Finally, a brief introduction to object detection and tracking will conclude the lecture.
2) Introduction to Machine Learning,  Wednesday 20th May 2020, 17:45-18:30 EEST

Summary: This lecture will cover the basic concepts of Machine Learning.  Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.  Classification, regression. Object detection, Object tracking. Clustering. Dimensionality reduction, data retrieval. Artificial Neural Networks. Adversarial Machine Learning. Generative Machine Learning. Temporal Machine learning (Recurrent Neural Networks). Continual Learning (few-shot learning, online learning). Reinforcement Learning. Adaptive learning (Knowledge Distillation, Domain adaptation, Transfer learning, Activation Pattern Analysis, Federated learning/Collaborative learning, Ensemble learning). Precise mathematical definitions of ML tasks will be presented.

Lecturer: 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, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. His current interests are in the areas of machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 860 papers, contributed in 44 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 69 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 41 such projects. He has 31000+ citations to his work and h-index 83+ (Google Scholar). Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: and is principal investigator (AUTH)  in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative

Lecturing record of Prof. I. Pitas: He 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 90 invited/keynote lectures in prestigious international Conferences and top Universities worldwide. He run 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.

Relevant links: a) Prof. I. Pitas:  b) AIIA Lab

General information: Lectures will consist primarily of live lecture streaming and PPT slides. Attendees (registrants) need no special computer equipment for attending the lecture. They will receive the lecture PDF before each lecture and will have the ability to ask questions real-time. Audience should have basic University-level undergraduate knowledge of any science or engineering department (calculus, probabilities, programming, that are typical e.g., in any ECE, CS, EE undergraduate program).  More advanced  knowledge (signals and systems, optimization theory, machine learning) is very helpful but nor required.

These two lectures are part of a 14 lecture CVML web course ‘Computer vision and machine learning for autonomous systems’ (April-June 2020):

Introduction to autonomous systems                                                               (delivered 25th April 2020)

Introduction to computer vision                                                                        (delivered 25th April 2020)

Image acquisition, camera geometry                                                                (delivered   2nd May 2020)

Stereo and Multiview imaging                                                                            (delivered   9th May 2020)

Structure from Motion                                                                                   (delivered   9th May 2020)

2D convolution and correlation algorithms

Motion estimation

Introduction to Machine Learning

Introduction to neural networks, Perceptron, backpropagation

Deep neural networks, Convolutional NNs

Deep learning for object/target detection

Object tracking

Localization and mapping

Fast convolution algorithms. CVML programming tools.

Sincerely yours

Prof. Ioannis Pitas

Director of Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece

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