Technologies de l'information et de la communication

Résumé

upright=1.7|thumb|Aperçu schématique des grandes composantes des Technologies de l'information et de la communication (TICS), et du cloud computing tel qu'émergeant au début des années 2000 Les technologies de l'information et de la communication ou techniques de l'information et de la communication (TIC, transcription de l'anglais information and communication technologies, ICT) sont, principalement dans le monde universitaire, le domaine de la télématique, c'est-à-dire les techniques de l'informatique, de l'audiovisuel, des multimédias, d'Internet et des télécommunications qui permettent aux utilisateurs de communiquer, d'accéder aux sources d'information, de stocker, de manipuler, de produire et de transmettre l'information sous différentes formes : texte, musique, son, , vidéo et interface graphique interactive (IHM). Les textes juridiques et réglementaires utilisent la locution « communications électroniques ». Histoire Après les premiers pas vers une société de l'inf

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https://fr.wikipedia.org/wiki/Technologies_de_l'information_et_de_la_communication

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Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics as diverse as mathematical reasoning, combinatorics, discrete structures & algorithmic thinking.

This course will cover all the aspects of product design and system engineering from learning relevant methods to the actual implementation in a hands-on practice of product development.

This course provides an overview of information security and privacy topics. It introduces students to the knowledge and tools they will need to deal with the security/privacy challenges they are likely to encounter in today's Big Data world. The tools are illustrated with relevant applications.

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In today's world, there is no shortage of disruptors acting on various professional domains. The Fourth Industrial Revolution, with its AI-driven and automation-focused technologies, has fundamentally changed many domains -- particularly the Information and Communication Technologies (ICT) domain -- and continues to do so. The COVID-19 pandemic and its ensuing lockdowns have resulted in dramatic change in the workplace. These disruptors are fast-acting and rapidly change the skills landscape of many professional domains, introducing new skills and rendering old ones obsolete. In such a situation, it is imperative for educational institutions to keep their curricula updated in order to (re)train the workforce with the right set of skills for the new economy. However, most of the methodologies commonly used for analyzing training needs and effecting curricular change were developed during a time when the pace of change was slower, and thus have trouble quickly and effectively responding to today's rapid changes. As a result, the development of new approaches for the analysis of training needs is of utmost importance.The present dissertation is the result of an effort to develop such new approaches, using different types of data and approaching the problem from different perspectives. These approaches use big data methods on data sources that are large, pre-existing, and continually updated in order to identify the skill needs of the present and/or predict the skill needs of the future for various professions. Focusing on the ICT domain but also making forays into vocational domains, the present work examines the usefulness and feasibility of methodologies such as the estimation of topical difficulty, the prediction of the appearance of new skills, and the prediction of emerging skills (less popular skills expected to experience a surge in hiring demand) for the identification of training needs. The findings of this dissertation are twofold. Firstly, they demonstrate the pivotal role that decentralized educational platforms such as Stack Overflow (an online Q&A platform for ICT) and Udemy (a MOOC platform where anyone can create a course) play in lifelong learning, essentially making the case for a policy of sponsorship and promotion of such platforms in other professional domains. Secondly and more importantly, the results showcase the feasibility of predicting the emerging skills of the near future. The successful emerging skills methodology can therefore be used to provide valuable insights to training providers at a moment's notice, giving them time to prepare for the skills landscape of the future in advance. Thus, AI can be used to help solve the challenges it creates for educational institutions and the workforce. Future work, both in the form of policymaking and in the form of research, can help capitalize and expand on the results of this dissertation, furthering the cause of rapid curricular change.

EPFL2022

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2006

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Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing.

Springer Verlag2008

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EPFL2022