384 lines
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HTML
384 lines
23 KiB
HTML
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content="Página do Encontro Nacional de Estudantes de Informática 2019, no Instituto Superior de Engenharia de Coimbra (ISEC) a realizar-se entre 12 e 15 de abril de 2019. O ENEI pretende oferecer aos estudantes de todo o país um evento que promove a aprendizagem, a troca de ideias e o convívio, enquanto tenta criar fortes ligações com o mundo empresarial.">
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<!-- apresentation section -->
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<div class="career-name">Artificial Intelligence<span>Powered by:</span></div>
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<img src="imgs/logos/critical-sponsor.png" alt="Critical Software"
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style="background-color: white;padding: 20px;" />
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<div class="pre-h1">Career Path</div>
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<h1>Artificial Intelligence</h1>
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<hr>
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<p>O ENEI19 não só vai ter um programa geral como, também, vai ter 5 Career Paths!
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Vais poder escolher o que mais se identifica contigo e ter acesso a todas as conferências sobre
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essa temática!.</p>
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<p>Podes fazer a tua inscrição no Career Path que mais gostares através da nossa app!</p>
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<p>Mas não te preocupes, apesar de te inscreveres num determinado Career Path, poderás participar em
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palestras ou workshops de Career Paths diferentes.</p>
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<h1>Programa</h1>
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<div class="event-time">11:15 - 13:30</div>
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<div class="event-type">Workshop</div>
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<h3>Licínio Oliveira</h3>
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<div class="event-details"><span class="icon-company"></span>Critical Software</div>
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<!--<button class="modal-link event-description"
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data-modalLink="modal-80"><span class="arrow">➔</span>Ver
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mais detalhes</button>-->
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<div class="event-time">15:30 - 16:30</div>
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<div
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class="col-lg-8 col-lg-offset-0 col-sm-offset-2 col-xs-offset-2 event-details">
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<div class="event-type">Palestra</div>
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<h3>Eduardo Pereira</h3>
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<div class="event-details"><span class="icon-company"></span> Delloite
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</div>
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<div class="row event-data">
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<div class="col-lg-4">
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<div class="event-time">15:15 - 16:15</div>
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<div
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class="col-lg-8 col-lg-offset-0 col-sm-offset-2 col-xs-offset-2 event-details">
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<div class="event-type">Palestra</div>
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<h2>Will Deep Convolutional Neural Networks and Reinforcement Learning
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lead to Artificial General Intelligence?</h2>
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<h3>Arlindo Oliveira</h3>
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<div class="event-details"><span class="icon-company"></span>Instituto
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Superior Técnico</div>
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<button class="modal-link event-description"
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data-modalLink="modal-84"><span class="arrow">➔</span>Ver
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mais detalhes</button>
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</div>
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</div>
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</div>
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</section>
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<!-- oradores section -->
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<section id="speakers">
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<div class="container">
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<div class="row">
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<div class="col-lg-6 col-lg-offset-3 col-sm-12 col-sm-offset-0 col-xs-10 col-xs-offset-1">
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<h1>Speakers</h1>
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<hr>
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</div>
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</div>
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<div class="row">
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<div class="col-lg-3 col-sm-6">
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<a href="https://www.criticalsoftware.com/" title="Licínio Oliveira" target="_blank" class="speaker-button">
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<div class="speaker-image">
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<img src="imgs/speakers-image/person.png" alt="Licínio Oliveira">
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<img src="imgs/sponsor-images/critical-sponsor.png" alt="ubiwhere" class="company-image">
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</div>
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<h2 class="speaker-name">Licínio Oliveira </h2>
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<div class="speaker-company">Critical Software</div>
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</a>
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</div>
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<div class="col-lg-3 col-sm-6">
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<a href="https://www2.deloitte.com/pt/pt.html" title="Eduardo Pereira" target="_blank"
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class="speaker-button">
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<div class="speaker-image">
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<img src="imgs/speakers-image/eduardo.png" alt="Eduardo Pereira">
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<img src="imgs/sponsor-images/deloitte-sponsor.png" alt="Deloitte" class="company-image">
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</div>
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<h2 class="speaker-name">Eduardo Pereira</h2>
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<div class="speaker-company">Delloite</div>
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</a>
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</div>
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<div class="col-lg-3 col-sm-6">
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<a href="https://tecnico.ulisboa.pt/pt/" title="Arlindo Oliveira" target="_blank"
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class="speaker-button">
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<div class="speaker-image">
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<img src="imgs/speakers-image/arlindo.png">
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<img src="imgs/speakers-company/ist.jpg" alt="Instituto Superior Técnico" class="company-image">
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</div>
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<h2 class="speaker-name">Arlindo Oliveira</h2>
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<div class="speaker-company">Instituto Superior Técnico</div>
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</a>
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</div>
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<p>© 2019 ENEI . Todos os direitos reservados</p>
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<h2>Description</h2>
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<p>(Critical)</p>
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<h2>Speaker's Bio</h2>
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<h3>Licínio Oliveira</h3>
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<p>Sem descrição
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</p>
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</div>
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<div class="modal-container" id="modal-81" data-status="closed">
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<button class="modal-close" data-modalContainer="modal-81"><span class="icon-close"></span></button>
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<h2>Speaker's Bio</h2>
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<h3>EDUARDO PEREIRA</h3>
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<p>Eduardo joined Deloitte PT in 2018 as Lead Specialist of Data Science / AI. He is leading the advanced
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analytics team at Deloitte in the financial services sector with the main aim to provide top-class solutions
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based on the combination of machine learning, computer vision, and natural language techniques to their
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clients. In his role, he is also coaching and mentoring internal staff, as well as master students, keeping
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a tight link with several universities and research centers. He is also focus on providing data-driven
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digitalization solutions within Deloitte PT. He has over 12 years’ experience working on computer graphics,
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computer vision, natural language processing and machine learning within surveillance, retail, forensic,
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entertainment, manufacturing, biology, finance and smart environments/cities. Most recently he was senior
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data scientist at UTRC in Cork-Ireland. Before that he spent four years as a machine learning researcher at
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INESCTEC. He worked on a mid-size and a startup companies as responsible for the computer vision/machine
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learning solutions, and he cofounded his own startup in the field of videogames and digital interactive
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solutions. Eduardo holds a PhD in Computer Vision/Machine Learning from the University of Porto-Portugal,
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with collaboration with UT Austin-USA.</p>
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</div>
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||
</div>
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||
|
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<div class="modal-container" id="modal-82" data-status="closed">
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<button class="modal-close" data-modalContainer="modal-82"><span class="icon-close"></span></button>
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<h2>Descrição</h2>
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<p>Atualmente existe um hype de Big Data entre as organizações, contudo transformar o seu potencial em real
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impacto de negócio tem-se provado um desafio. Desenvolver modelos precisos e confiáveis é um primeiro
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passo, no entanto colocá-los em produção e fazer a sua gestão diariamente - descobrindo conhecimento único -
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é outro nível. Os modelos e automação preditivos serão fundamentais na operacionalização de todo o processo,
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ajudando a orquestrar e gerir o processamento de todos os dados dentro de uma organização. Este deve ser o
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alvo final de uma estratégia de Big Data bem-sucedida.
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Neste workshop vamos nos concentramos numa dimensão crítica - a Excelência Operacional. A análise de grandes
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volumes de dados operacionais pode suportar os esforços dos CSP’s na expansão e controlo da infraestrutura
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atual. Através da correlação de métricas de uso da rede e atributos de utilizadores, além de dados de
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tráfego e localização, ajudará de forma proactiva a detetar ou prever possíveis problemas e obstáculos em
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tempo real.
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O principal objetivo deste workshop é desenhar e desenvolver um algoritmo capaz de realizar a deteção de
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anomalias por categoria, usando para o efeito dados operacionais de comunicações móveis. Com base nas
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mensagens enviadas ou recebidas pelos cartões SIM, a solução proposta deve ser capaz de detetar padrões em
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altos volumes de mensagens e fornecer informações sobre as suas causas.
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</p>
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<h2>Requesitos</h2>
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<p>Computador pessoal com as seguintes ferramentas instaladas:</p>
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<p>-Jupyter Notebook</p>
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<p>-Python 3.6</p>
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<p>-ScikitLearn</p>
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<p>-Matplotlib</p>
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<p>-Pandas</p>
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|
||
</div>
|
||
|
||
<div class="modal-container" id="modal-83" data-status="closed">
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||
<button class="modal-close" data-modalContainer="modal-83"><span class="icon-close"></span></button>
|
||
<h2>Descrição</h2>
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<p>A Aguardar descrição e nome.</p>
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||
</div>
|
||
<div class="modal-container" id="modal-84" data-status="closed">
|
||
<button class="modal-close" data-modalContainer="modal-84"><span class="icon-close"></span></button>
|
||
<h2>Descrição</h2>
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||
<p>"Artificial Intelligence, and its diverse subfields, including machine learning, has been the subject of
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intense study for more than a half a century. Recent advances in machine learning, jointly known as deep
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learning, have partially closed the gap that exists between the abilities of naturally intelligent systems
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(i.e., brains) and artificially intelligent ones in problems related with perception. Additionally, some
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problems that have been deemed very hard to solve, like learning to play the game of Go from scratch or
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mastering complex strategy games have fallen to approaches that combine deep reinforcement learning with
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efficient computation methods. Still, the depth of understanding of these systems is somewhat limited, and
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many counterexamples exist that show that DCNNs (deep convolutional neural networks) are still very far from
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approaching human abilities even in simple perception problems. In this seminar, I will lead an interactive
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discussion about the power and limitations of DCNNs and whether this technology will lead the way to
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artificial general intelligence (AGI) or will simply become one more tool in the practitioners’ toolbox.
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Active involvement from the audience will be expected."</p>
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<h2>Speaker's Bio</h2>
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<h3>Arlindo Oliveira</h3>
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<p>"Arlindo Oliveira was born in Angola and has lived in Mozambique, Portugal, Switzerland and California. He
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obtained his BSc and MSc degrees from Instituto Superior Técnico (IST) and his PhD degree from the
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University of California at Berkeley, all in Electrical Engineering and Computer Science. He was a
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researcher at CERN and at the Berkeley Cadence Laboratories. He is a professor at the computer science and
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engineering department of IST and a researcher at INESC-ID. He authored three books, translated in different
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languages, and more than 150 articles in international conferences and journals. He has been on the boards
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of a number of companies and institutions and is a past president of INESC-ID and of the Portuguese
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Association for Artificial Intelligence. He is a member of the IEEE and of the Portuguese Academy of
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Engineering. He became president of Instituto Superior Técnico in 2012.
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</p>
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||
</div>
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||
<div class="modal-container" id="modal-85" data-status="closed">
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||
<button class="modal-close" data-modalContainer="modal-85"><span class="icon-close"></span></button>
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||
<h2>Descrição</h2>
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<p>Neste workshop, veremos algumas das diversas fases que estão associadas ao desenvolvimento de um modelo de
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deteção de fraude baseado em Machine Learning, desde a recolha e tratamento de dados, feature engineering,
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modelação e avaliação. Para tal vamos explorar essencialmente packages da linguagem de programação Python,
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como pandas, numpy e scikit-learn. Material necessário: portátil.</p>
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<h2>Speaker's Bio</h2>
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<h3>Fábio Pinto</h3>
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<p>Fábio Pinto é Research Data Scientist na Feedzai, empresa líder de mercado no combate à fraude com recurso a
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Inteligência Artificial. Doutorado em Engenharia Informática pela Faculdade de Engenharia da Universidade do
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Porto, a sua investigação têm-se focado essencialmente em Automatic Machine Learning (autoML), tendo
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publicado em diversas conferências e revistas da área, como a Machine Learning Journal.
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</p>
|
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||
gtag('config', 'UA-130588243-1');
|
||
</script>
|
||
</body>
|
||
|
||
</html> |