Goal of IASD
The IASD master program aims at training students with a strong theoretical knowledge and a practical experience in AI and Data Science. Its goal is to provide students with a solid and general understanding of modern artificial intelligence, so they can build robust and reliable AI systems.
Machine learning, deep learning, optimization, databases, knowledge representation, automatic reasoning. planning and a variety of optional courses.
Academic career, Ph.D. (PSL, CNRS, Inria, CEA, INRA etc.) and a career in research and development (Google, Facebook, Criteo, Keyrus, Amazon, 1000mercis, IBM, Havas, AXA, BNP Paribas, …).
A year at IASD
The IASD Master program starts with a semester of lectures and exercise sessions (from September to December) followed by optional courses (from January to April) and a research internship (from April to September). The courses are divided into two semesters. During the first semester, from September to December, students follow 6 mandatory courses about artificial intelligence and data science, for a total of 30 ECTS. During the second semester of optional courses, from January to April, students can choose at least 6 advanced courses from a wide selection of optional courses, for a total of 18 ECTS. The internship, from April to August, is worth 12 ECTS, and is carried out in an academic or industrial research laboratory and ends with the writing of a thesis and a defence in September. For students who need it, refresher courses on the basics of mathematics and computer science are scheduled before the start of the core curriculum in September.
Who should apply?
The IASD Master program is intended for students who are interested in research and development careers in the field of artificial intelligence and data science. IASD enrolls students with a strong academic record, who have already validated a first year of a master degree in computer science or applied mathematics. The Master also enrolls students in their final year of engineering school (or who have already obtained an engineering degree) if the courses match the requirements. Finally, a strong taste for AI and Data Science is mandatory!
A selection will be made to select the best applications, within the limit of the maximum number of students (about forty).
Olivier Cappé (École normale supérieure – PSL, CNRS) – Director
Benjamin Negrevergne (University Paris-Dauphine – PSL) – Associate-director.
Pierre Senellart (École normale supérieure – PSL) – Correspondent for ENS
Etienne Decencière (Mines de Paris – PSL) – Correspondent for Les Mines
Contact: Olivier Cappé (email@example.com)