pasobdenver.blogg.se - Artificial academy 2 reiedit

#Artificial academy 2 reiedit how to#

You will need to provide the following documents:

#Artificial academy 2 reiedit how to#

How to applyĪpplications can be submitted exclusively online. Language prerequisitesĮnglish: proof of English proficiency is required (TOEFL results, language course or language test), except for native speakers and students who previously studied in English.

Advanced Machine Learning and Autonomous AgentsĬompletion of the first year of a Master in computer science or related field at Institut Polytechnique de Paris or equivalent in France (Engineering School) or abroad.

Image mining and content-based retrieval.

Algorithmic information and artificial intelligence.

Basics of image processing and analysis.

Machine Learning: Shallow & Deep Learning.

Probabilistic Models and Machine Learning.

A course is validated when the student obtains a grade of 10/20 or higher. Students should not take two courses with overlapping topics, for details see the course descriptions.Īll Data AI courses are 24h, count 2.5 ECTS, and are validated by labs, presentations and/or exams.

TPT-DATAAI951 - AI Ethics (Maxwell Winston, Sophie Chabridon, Ada Diaconescu, Fabian Suchanek).

TPT-DATAAI941 Softskills seminar - Softskills seminar (M2 only) (Fabian Suchanek).

X-INF553 - Database management systems (Ioana Manolescu).

TPT-DATAAI922 - Big Data Processing (Louis Jachiet).

TSP-CSC5003-1 - Big data infrastructures (Bruno Defude).

TPT-DATAAI921 - Architectures for Big Data (Ioana Manolescu).

X-INF583 - Systems for Big Data (Angelos Anadiotis / Yanlei Diao).

TPT-SD206 - Logic & Knowledge representation (J.-L.

TPT-IA301 - Logics and Symbolic AI (Isabelle Bloch & Natalia Diaz).

TPT-DATAAI901 - Machine Learning (Filippo Miatto).

X-INF554 - Machine & Deep Learning Introduction (M.

TPT-DATAAI902 - Machine Learning: Shallow & Deep Learning (Mounim El Yacoubi).

Students must validate at least one course for each of the following groups, before the end of the Master year 2 (courses completed in M1 count as validated):

acquire a total of at least 30 ECTS courses (including the mandatory courses) with at least 25 ECTS in Data AI courses.fulfill all the Data AI mandatory course requirements (see below).To validate the Master Year 2, a student must accomplish the following: Solve theoretical and applied problems, present their work in oral presentations and written reports, analyze a bibliography and identify open research directions, work independently and in a team, identify and seek appropriate resources for advancing their work.Gain experience in using and developing data-supported smart services and tools for data-driven decision making, while exploring the technical and scientific challenges of processing large data and knowledge bases.Acquire the fundamental knowledge, technical skills and concrete applied methodologies to make machines more intelligent.The Master’s program aims to enable students to: In the second year, students will build more advanced knowledge and complete a research internship. Students can choose from a wide range of courses including mining large datasets, big data processing systems, reinforcement learning, GPU programming, semantic networks, cognitive modeling, self-organizing multi-agent systems, autonomous navigation for robots, text mining and image understanding, as well as ethics in AI. Students will acquire the basics of machine learning, logic, big data systems, and databases, before diving into applications in advanced machine learning, symbolic AI, swarm intelligence, natural language processing, visual computing, and robotics. The two-year Data Artificial Intelligence Master’s program covers artificial intelligence (AI) and large-scale data management.