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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><div class=WordSection1><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Dear Computer Vision/Machine Learning/Autonomous Systems students, engineers, scientists and enthusiasts,<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to </span><span style='font-family:"Calibri",sans-serif'>have <span style='color:black'>launch</span>ed<span style='color:black'> the live CVML Web lecture series <o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>that cover</span><span style='font-family:"Calibri",sans-serif'>s<span style='color:black'> very important Computer </span>V<span style='color:black'>ision/</span>M<span style='color:black'>achine </span>L<span style='color:black'>earning</span> <span style='color:black'>topics. Two </span>new upcoming 45 min <span style='color:black'>lectures will take place </span>soon<span style='color:black'>:<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>1) Motion estimation <o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>2) Introduction to Machine Learning<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>New!:</span></b><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>Date/time: </span>Wednesday 20th May 2020, <span style='color:black'> </span>17<span style='color:black'>:00-1</span>8:30<span style='color:black'> EE</span>S<span style='color:black'>T </span>for both lectures (7:00-8:30 am California time, 10:00-11:30 am New York time, 22:00-23:30 Beijing time).<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Registration can be done using the link: </span></b><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/"><b><span style='font-family:"Calibri",sans-serif'>http://icarus.csd.auth.gr/cvml-web-lecture-series/</span></b></a><b><span style='font-family:"Calibri",sans-serif'><o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>Registration for <span style='color:black'>asynchronous access to CVML live Web lecture material (video, pdf/ppt) </span>for any past/present lecture can be done using the link:<span style='color:black'> </span></span></b><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/"><b><span style='font-family:"Calibri",sans-serif'>http://icarus.csd.auth.gr/cvml-web-lecture-series/</span></b></a><b><span style='font-family:"Calibri",sans-serif;color:black'><o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Lecture abstract</span></b><b><span style='font-family:"Calibri",sans-serif'>s<o:p></o:p></span></b></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>1)</span></b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'> <strong><span style='font-family:"Calibri",sans-serif'>Motion estimation, </span></strong>Wednesday 20<sup>th</sup> May 2020, 17:00-17:45 EEST <o:p></o:p></span></h6><h6><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>Summary: </span></strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>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. <o:p></o:p></span></h6><p class=MsoNoSpacing><span class=tadv-color><o:p> </o:p></span></p><p class=MsoNoSpacing><b><o:p> </o:p></b></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>2) <strong><span style='font-family:"Calibri",sans-serif'>Introduction to Machine Learning,</span></strong></span></b><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black;font-weight:normal'> </span></strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>Wednesday 20<sup>th</sup> May 2020, 17:45-18:30 EEST <o:p></o:p></span></h6><p class=MsoPlainText style='text-align:justify'><b><span style='font-size:12.0pt;line-height:105%;color:black'>Summary: </span></b><span style='font-size:12.0pt;line-height:105%;color:black'>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.<o:p></o:p></span></p><p><b><span style='font-size:12.0pt;color:black'>Lecturer: </span></b><b><span style='font-size:12.0pt'>Prof. Ioannis Pitas </span></b><span style='font-size:12.0pt'>(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). <span style='color:black'>Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: </span></span><a href="https://multidrone.eu/"><span style='font-size:12.0pt'>https://multidrone.eu/</span></a><span style='font-size:12.0pt'> <span style='color:black'>and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative </span></span><a href="https://ieeeasi.signalprocessingsociety.org/"><span style='font-size:12.0pt'>https://ieeeasi.signalprocessingsociety.org/</span></a><span style='font-size:12.0pt;color:black'>.</span><span style='font-size:12.0pt'><o:p></o:p></span></p><p><b><span style='font-size:12.0pt;color:black'>Lecturing record of Prof. I. Pitas:</span></b><span style='font-size:12.0pt;color:black'> 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), </span><span lang=EN-GB style='font-size:12.0pt;color:black'> University of Bristol (UK), Tampere University of Technology (Finland), Yonsei University (Korea), Erlangen-Nurnberg University (Germany),</span><span style='font-size:12.0pt;color:black'> National University of Malaysia, </span><span lang=EN-GB style='font-size:12.0pt;color:black'>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.</span><span style='font-size:12.0pt'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'>Relevant links: a) Prof. <span style='color:black'>I. Pitas: </span></span><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el"><span style='font-family:"Calibri",sans-serif'>https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</span></a><span style='font-family:"Calibri",sans-serif'> b) <span style='color:black'>AIIA Lab </span></span><a href="http://www.aiia.csd.auth.gr"><span style='font-family:"Calibri",sans-serif'>www.aiia.csd.auth.gr</span></a><span class=MsoHyperlink><span style='font-family:"Calibri",sans-serif;color:windowtext;text-decoration:none'><o:p></o:p></span></span></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif'>General information:</span></b><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>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.<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>These two lectures are part of a 14 lecture <b>CVML web course <span class=tadv-color>‘Computer vision and machine learning for autonomous systems’</span></b><span class=tadv-color> (April-June 2020):</span></span><span class=tadv-color><o:p></o:p></span></p><p class=MsoNoSpacing><o:p> </o:p></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to autonomous systems </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to computer vision </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Image acquisition, camera geometry </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered 2<sup>nd</sup> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Stereo and Multiview imaging </span><span style='font-family:"Calibri",sans-serif'> <span style='color:black'>(delivered </span>9<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Structure from Motion (delivered </span><span style='font-family:"Calibri",sans-serif'>9<sup>th</sup><span style='color:black'> May 2020)<o:p></o:p></span></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>2D convolution and correlation algorithms<o:p></o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Motion estimation <o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to Machine Learning<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Introduction to neural networks, Perceptron, backpropagation<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Deep neural networks, Convolutional NNs<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Deep learning for object/target detection<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Object tracking <o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Localization and mapping<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Fast convolution algorithms. CVML programming tools.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Sincerely yours<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Prof. Ioannis Pitas<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif;color:black'>Director of Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece<o:p></o:p></span></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='font-family:"Calibri",sans-serif;color:black'>Post scriptum: To stay current on CVMl matters, you may want to register to the CVML email list, following instructions in </span></b><a href="https://lists.auth.gr/sympa/info/cvml"><b><span style='font-family:"Calibri",sans-serif'>https://lists.auth.gr/sympa/info/cvml</span></b></a><b><span style='font-family:"Calibri",sans-serif'><o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='font-family:"Calibri",sans-serif'><o:p> </o:p></span></p><div id=DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><o:p> </o:p></p><table class=MsoNormalTable border=1 cellpadding=0 style='border:none;border-top:solid #D3D4DE 1.0pt'><tr><td width=55 style='width:41.25pt;border:none;padding:9.75pt .75pt .75pt .75pt'><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><a href="https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient&utm_term=icon" target="_blank"><span style='text-decoration:none'><img border=0 width=46 height=29 style='width:.4791in;height:.302in' id="_x0000_i1025" src="https://ipmcdn.avast.com/images/icons/icon-envelope-tick-round-orange-animated-no-repeat-v1.gif"></span></a><o:p></o:p></p></td><td width=470 style='width:352.5pt;border:none;padding:9.0pt .75pt .75pt .75pt'><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:13.5pt'><span style='font-size:10.0pt;font-family:"Arial",sans-serif;color:#41424E'>Virus-free. </span><a href="https://www.avast.com/sig-email?utm_medium=email&utm_source=link&utm_campaign=sig-email&utm_content=emailclient&utm_term=link" target="_blank"><span style='font-size:10.0pt;font-family:"Arial",sans-serif;color:#4453EA'>www.avast.com</span></a><span style='font-size:10.0pt;font-family:"Arial",sans-serif;color:#41424E'> <o:p></o:p></span></p></td></tr></table><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><o:p> </o:p></p></div></div></body></html>