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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='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='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Artificial Intelligence and Information analysis (AIIA) Lab, Aristotle University of Thessaloniki, Greece is proud to have launched the live CVML Web lecture series <o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>that covers very important Computer Vision/Machine Learning topics. Two new upcoming 45 min lectures will take place soon:<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='color:black'>1) Artificial Neural Networks. Perceptron<o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='color:black'>2) Multilayer perceptron. Backpropagation<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Date/time: Wednesday 27th 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).<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='color:black'>Registration can be done using the link: </span><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><o:p></o:p></b></p><p class=MsoNoSpacing><b><span style='color:black'>Registration for asynchronous access to CVML live Web lecture material (video, pdf/ppt) for any past/present lecture can be done using the link: </span><a href="http://icarus.csd.auth.gr/cvml-web-lecture-series/">http://icarus.csd.auth.gr/cvml-web-lecture-series/</a><span style='color:black'><o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='color:black'>Lecture abstracts<o:p></o:p></span></b></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'>1)</span></b><b><span style='font-family:"Times New Roman",serif;color:black'> Artificial Neural Networks. Perceptron</span></b><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri Light",sans-serif;color:black'>, </span></strong><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'>Wednesday 27<sup>th</sup> May 2020, 17:00-17:45 EEST <o:p></o:p></span></h6><p class=MsoPlainText style='text-align:justify'><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri",sans-serif;color:black'>Summary: </span></strong><span style='font-family:"Times New Roman",serif;color:black'>This lecture will cover the basic concepts of Artificial Neural Networks (ANNs): Biological neural models, Perceptron, Activation functions, Loss types, Steepest Gradient Descent, On-line Perceptron training, Batch Perceptron training.</span><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'><o:p></o:p></span></p><h6><b><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'>2) </span></b><b><span style='font-family:"Times New Roman",serif;color:black'>Multilayer perceptron. Backpropagation</span></b><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri Light",sans-serif;color:black'>,</span></strong><strong><span style='font-size:12.0pt;line-height:105%;font-family:"Calibri Light",sans-serif;color:black;font-weight:normal'> </span></strong><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'>Wednesday 27<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%;font-family:"Times New Roman",serif;color:black'>Summary: </span></b><span style='font-family:"Times New Roman",serif;color:black'>This lecture will cover the basic concepts of Multi-Layer Perceptron (MLP), Training MLP neural networks, Activation functions, Loss types, Gradient descent, Error Backpropagation, Stochastic Gradient Descent, Adaptive Learning Rate Algorithms, Regularization, Evaluation, Generalization.</span><span style='font-size:12.0pt;line-height:105%;font-family:"Times New Roman",serif;color:black'><o:p></o:p></span></p><p><b><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>Lecturer: Prof. Ioannis Pitas </span></b><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>(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: <a href="https://multidrone.eu/">https://multidrone.eu/</a></span><span style='font-size:12.0pt;font-family:"Times New Roman",serif'> <span style='color:black'>and is principal investigator (AUTH) in H2020 projects Aerial Core and AI4Media. He is chair of the Autonomous Systems initiative <a href="https://ieeeasi.signalprocessingsociety.org/">https://ieeeasi.signalprocessingsociety.org/</a>.</span><o:p></o:p></span></p><p><b><span style='font-size:12.0pt;font-family:"Times New Roman",serif;color:black'>Lecturing record of Prof. I. Pitas:</span></b><span style='font-size:12.0pt;font-family:"Times New Roman",serif;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;font-family:"Times New Roman",serif;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;font-family:"Times New Roman",serif;color:black'> National University of Malaysia, </span><span lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman",serif;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;font-family:"Times New Roman",serif;color:black'><o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Relevant links: a) Prof. I. Pitas: </span><span class=MsoHyperlink><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el">https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</a></span> <span style='color:black'>b) AIIA Lab </span><span class=MsoHyperlink><a href="http://www.aiia.csd.auth.gr">www.aiia.csd.auth.gr</a></span><span class=MsoHyperlink><span style='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='color:black'>General information:</span></b><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></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='color:black'>These two lectures are part of a 15 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='color:black'>Introduction to autonomous systems (delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Introduction to computer vision (delivered 25<sup>th</sup> April 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Image acquisition, camera geometry (delivered 2<sup>nd</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Stereo and Multiview imaging (delivered 2<sup>nd </sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Structure from Motion (delivered 9<sup>th</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>2D convolution and correlation algorithms (delivered 9<sup>th</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Motion estimation (delivered 20<sup>th</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Introduction to Machine Learning (delivered 20<sup>th</sup> May 2020)<o:p></o:p></span></p><p class=MsoNoSpacing><b><span style='color:black'>Artificial Neural Networks. Perceptron<o:p></o:p></span></b></p><p class=MsoNoSpacing><b><span style='color:black'>Multilayer perceptron. Backpropagation<o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='color:black'>Deep neural networks, Convolutional NNs<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Deep learning for object/target detection<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Object tracking <o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Localization and mapping<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Fast convolution algorithms. CVML programming tools.<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Sincerely yours<o:p></o:p></span></p><p class=MsoNoSpacing><span style='color:black'>Prof. Ioannis Pitas<o:p></o:p></span></p><p class=MsoNoSpacing><span style='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='color:black'><o:p> </o:p></span></p><p class=MsoNoSpacing><b><span style='color:black'>Post scriptum: To stay current on CVMl matters, you may want to register to the CVML email list, following instructions in <a href="https://lists.auth.gr/sympa/info/cvml"><span style='color:black'>https://lists.auth.gr/sympa/info/cvml</span></a><o:p></o:p></span></b></p><p class=MsoNoSpacing><span style='color:black'><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'><span style='font-family:"Times New Roman",serif;color:black'><o:p> </o:p></span></p><p class=MsoNormal style='margin-bottom:0in;margin-bottom:.0001pt;line-height:normal'><span style='font-family:"Times New Roman",serif;color:black'><o:p> </o:p></span></p></div></div></body></html>