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</o:shapelayout></xml><![endif]--></head><body lang=EL link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoPlainText><span lang=EN-US>Dear Machine Learning and Deep Neural Networks</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>engineers, scientists and enthusiasts,<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>you are welcomed to register in th</span><span lang=EN-US style='color:windowtext'>is <b>CVML Short e-course on ‘</b></span><b><span lang=EN-US>Machine Learning and Deep Neural Networks</span></b><b><span lang=EN-US style='color:windowtext'>’, 2</span></b><b><span lang=EN-US>7-</span></b><b><span lang=EN-US style='color:windowtext'>2</span></b><b><span lang=EN-US>8<sup>th</sup> </span></b><b><span lang=EN-US style='color:windowtext'>April</span></b><b><span lang=EN-US> 2021: </span></b><span lang=EN-US style='color:windowtext'><a href="https://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/">https://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/</a><o:p></o:p></span></p><p class=MsoPlainText><b><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></b></p><p class=MsoPlainText style='text-align:justify'><span lang=EN-US>It will take place as a </span><span lang=EN-US style='color:windowtext'>two-day </span><span lang=EN-US>e-course (due to COVID-19 circumstances), hosted by the Aristotle University of Thessaloniki (AUTH),</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>Thessaloniki, Greece, providing a series of live lectures delivered through</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>a tele-education platform. They will be complemented with on-line video</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>recorded lectures and lecture pdfs, to facilitate international participants</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>having time difference issues and to enable </span><span lang=EN-US style='color:windowtext'>you </span><span lang=EN-US>to study at own pace.</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>You can also self-assess your knowledge</span><span lang=EN-US style='color:windowtext'>, </span><span lang=EN-US>by filling appropriate questionnaires (one per lecture). You will be provided programming exercises to improve your programming skills. </span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p><p class=MsoPlainText style='text-align:justify'><span lang=EN-US style='color:windowtext'>It is part of the very successful CVML short course series that took place in the last three years. <o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></p><h1><span lang=EN-US style='font-size:11.0pt'>Course description ‘Machine Learning and Deep Neural Networks’<o:p></o:p></span></h1><p class=MsoPlainText><span lang=EN-US>The short e-course consists of <b>16 1-hour </b></span><b><span lang=EN-US style='color:windowtext'>live </span></b><b><span lang=EN-US>lectures</span></b><span lang=EN-US> organized in two </span><span lang=EN-US style='color:windowtext'>P</span><span lang=EN-US>arts</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>(</span><span lang=EN-US style='color:windowtext'>1 Part </span><span lang=EN-US>per day):<o:p></o:p></span></p><p class=MsoNormal><b><span lang=EN-US>Part A lectures (8 hours)</span></b><span lang=EN-US> provide an in-depth presentation of Deep Neural Networks, which are at the forefront of AI advances today, starting with introduction to Machine Learning. Then the cornerstone DNN theory and technologies are presented: a) Artificial Neural Networks, Perceptron; b) Multilayer perceptron, Backpropagation; c) Deep neural networks. Both data classification and regression problems are treated. Convolutional NNs; d) Recurrent Neural Networks; e) Attention and Transformers. Applications follow in several image analysis, computer vision and autonomous system applications, notably: a) Deep learning for object detection and b) Deep Semantic Image Segmentation. Finally, Generative Adversarial Networks are presented that promise to revolutionize the way we create media/arts, while seriously threatening our democracy with fake data creation and spread.</span><span lang=EN-US style='font-size:12.0pt;font-family:"Times New Roman",serif'><o:p></o:p></span></p><p class=MsoNormal><b><span lang=EN-US>Part B lectures (8 hours)</span></b><span lang=EN-US> provide fan in-depth presentation of Machine Learning to complement DNNs. Unsupervised Learning (Data Clustering) is first detailed, allowing us to find structure and extract concepts/knowledge from huge high-dimensionality data. Then Supervised Learning (Data Classification) techniques are presented, notably: a) Decision surfaces (whose special case is DNNs and SVMs) and b) Distance based classification. Dimensionality reduction techniques are overviewed, allowing us to visualize high-dimensionality data found in most applications, ranging from Medicine to Financial Engineering. Kernel methods are presented that can boost performance of any linear ML operation (e.g., PCA, K-means etc). Bayesian learning provides a unified theoretical framework that can encompass many of the ML approaches. Deep Reinforcement Learning is also presented, as it is an essential element in novel Robotics/Control and other decision-making application domains. Finally, CVML programming tools (e.g., DNN frameworks, BLAS/cuBLAS, DNN and CV libraries) are overviewed, as they allow fast application of all the above knowledge in almost any application domain.<o:p></o:p></span></p><p class=MsoNormal style='text-align:justify'><span lang=EN-US><o:p> </o:p></span></p><h6><strong><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'><o:p> </o:p></span></strong></h6><p class=MsoNormal><a name="_Hlk60825748"><b><span lang=EN-US>Course lectures</span></b><o:p></o:p></a></p><span style='mso-bookmark:_Hlk60825748'></span><h6><strong><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'>Part A: </span></strong><b><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'>Deep Neural networks<strong><span style='font-family:"Calibri",sans-serif'> (first day, 8 lectures)</span></strong><o:p></o:p></span></b></h6><ol style='margin-top:0in' start=1 type=1><li class=MsoListParagraphCxSpFirst style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Introduction to Machine Learning<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Artificial Neural Networks, Perceptron<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Multilayer perceptron. Backpropagation<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Deep neural networks. Convolutional NNs<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Recurrent Neural Networks. LSTMs<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Attention and Transformers<o:p></o:p></span></li><li class=MsoListParagraphCxSpMiddle style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Deep learning for object detection<o:p></o:p></span></li><li class=MsoListParagraphCxSpLast style='margin-left:0in;mso-add-space:auto;mso-list:l2 level1 lfo3'><span lang=EN-US>Deep Semantic Image Segmentation<o:p></o:p></span></li></ol><h6><strong><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'>Part B: </span></strong><b><span lang=EN-US style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'>Machine Learning. Pattern Recognition <strong><span style='font-family:"Calibri",sans-serif'>(second day, 8 lectures)</span></strong></span></b><strong><span style='font-size:11.0pt;font-family:"Calibri",sans-serif;color:windowtext'><o:p></o:p></span></strong></h6><ol style='margin-top:0in' start=1 type=1><li class=MsoPlainText style='mso-list:l1 level1 lfo6'><span lang=EN-US style='color:windowtext'>Generative Adversarial Networks </span><o:p></o:p></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Data Clustering<o:p></o:p></span></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Decision Surfaces. Support Vector Machines<o:p></o:p></span></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Dimensionality Reduction<o:p></o:p></span></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Kernel Methods<o:p></o:p></span></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Bayesian Learning<o:p></o:p></span></li><li class=MsoPlainText style='color:windowtext;mso-list:l1 level1 lfo6'><span lang=EN-US>Deep Reinforcement Learning<o:p></o:p></span></li><li class=MsoPlainText style='mso-list:l1 level1 lfo6'><span lang=EN-US style='color:windowtext'>CVML Software Development Tools</span><b><span lang=EN-US><o:p></o:p></span></b></li></ol><p class=MsoPlainText><b><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></b></p><p class=MsoNormal><span lang=EN-US>Though independent, the attendees of this short e-course will greatly benefit by attending the CVML Short e-course on ‘Computer Vision for Autonomous Systems’ 5-6<sup>th</sup> May 2021:<o:p></o:p></span></p><p class=MsoPlainText><b><span lang=EN-US style='color:windowtext'><a href="http://icarus.csd.auth.gr/spring-cvml-short-course-computer-vision-for-autonomous-systems/">http://icarus.csd.auth.gr/spring-cvml-short-course-computer-vision-for-autonomous-systems/</a></span></b><b><span lang=EN-US><o:p></o:p></span></b></p><p class=MsoPlainText><b><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></b></p><p class=MsoPlainText><b><span lang=EN-US>You can use the following link for course registration:<o:p></o:p></span></b></p><p class=MsoPlainText><span lang=EN-US style='color:windowtext'><a href="http://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/">http://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Lecture topics, sample lecture ppts and videos</span><span lang=EN-US style='color:windowtext'>, self-assessment questionnaires and programming exercises</span><span lang=EN-US> can be found therein.<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>For questions, please contact: Ioanna Koroni <<a href="mailto:koroniioanna@csd.auth.gr">koroniioanna@csd.auth.gr</a>><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText style='text-align:justify'><span lang=EN-US>The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow,</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>Chair of the IEEE SPS Autonomous Systems Initiative, Director of the</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>University of Thessaloniki, Greece, Coordinator of the European Horizon2020</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>R&D project Multidrone. He is ranked 249-top Computer Science and</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>Electronics scientist internationally by Guide2research (2018). 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+.</span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>AUTH is ranked 153/182 internationally in Computer Science/Engineering,</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>respectively, in USNews ranking.</span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Relevant links:<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>1) Prof. I. Pitas:<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><a href="https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el">https://scholar.google.gr/citations?user=lWmGADwAAAAJ&hl=el</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>2) Horizon2020 EU funded R&D project Aerial-Core: <a href="https://aerial-core.eu/">https://aerial-core.eu/</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>3) Horizon2020 EU funded R&D project Multidrone: <a href="https://multidrone.eu/">https://multidrone.eu/</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>4) Horizon2020 EU funded R&D project AI4Media: <a href="https://ai4media.eu/">https://ai4media.eu/</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>5) AIIA Lab: <a href="https://aiia.csd.auth.gr/">https://aiia.csd.auth.gr/</a><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US style='color:windowtext'><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Sincerely yours</span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>Prof. I. Pitas<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>Director of the Artificial Intelligence and Information analysis Lab (AIIA</span><span lang=EN-US style='color:windowtext'> </span><span lang=EN-US>Lab)</span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US>Aristotle University of Thessaloniki, Greece<o:p></o:p></span></p><p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p><p class=MsoPlainText><span lang=EN-US>Post scriptum: To stay current on CVML matters, you may want to register in the CVML email list, following instructions in: <a href="https://lists.auth.gr/sympa/info/cvml">https://lists.auth.gr/sympa/info/cvml</a></span><span lang=EN-US style='color:windowtext'><o:p></o:p></span></p></div><div id="DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2"><br />
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