Everything you need to know about the Professional Master's degree in Computer Science - AI (and its internships)
This webinar presents the curriculum and scope of a Professional Master’s degree in Computer Science specializing in AI, as well as the proposed prerequisites, the courses and possible opportunities.
Event offered in French only
In this webinar for students and professionals alike, AI scientist Camille Besse will briefly review the curriculum and scope of a Professional Master’s degree in Computer Science specializing in Artificial Intelligence, as well as the desirable prerequisites and the courses. He will also provide details on the internship process and give some examples of the types of internships that have taken place in companies.
For possible industrial partners, he will review the calendar of internship proposals and how it is possible to offer an internship, even without the in-house expertise of an AI or data specialist. He will briefly present the financial aspect and the facilitating aspects offered by the partnership between IID and Mitacs.
For more information about the program
This webinar is presented by IID (Institute Intelligence and Data) in collaboration with the Faculty of Science and Engineering of Université Laval
Speaker
Scientifique de données, Institut intelligence et données (IID)
Camille Besse est scientifique de l’IA à l’Institut intelligence et données (IID) de l’Université Laval.
Détenteur d’un doctorat sur la prise de décision séquentielle en contexte incertain et de plusieurs années d’expérience, tant en recherche et encadrement d’étudiants, qu’en enseignement, Camille Besse s’est spécialisé en accompagnement et développement de solutions adaptées aux besoins de ses interlocuteurs de tous horizons, industriels, comme académiques. Il soutien le développement du programme de stage de la maitrise professionnelle en informatique – intelligence artificielle depuis sa création et travaille activement à son essor et à sa pérennisation. Il s’intéresse également à la sensibilisation aux enjeux éthique de l’utilisation secondaire des données, notamment en milieu industriel.
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