Objectives of Programme's Learning Outcomes

• Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
• Understand the stages of an engineering approach
• Identify and describe the problem to be solved and the functional need (of prospective clients) to be met considering the state of technology
• Design, evaluate and optimise solutions addressing the problem
• Implement a chosen solution in the form of a drawing, a schema, a plan, a model, a prototype, software and/or digital model
• Communicate the approach, results and prospects to a client or a board
• Identify and acquire the information and skills needed to solve the problem
• Understand the theoretical and methodological fundamentals in science and engineering to solve problems involving these disciplines
• Identify, describe and explain basic scientific and mathematical principles
• Identify, describe and explain the basic principles of engineering particularly in their specialising field
• Select and rigorously apply knowledge, tools and methods in sciences and engineering to solve problems involving these disciplines
• Understand the fundamentals of project management to carry out a set project, individually or as part of a team
• Adapt the approach and achievements taking into account the feedback received
• Respect deadlines and timescales
• Collaborate, work in a team
• Interact effectively with other students to carry out collaborative projects.
• Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
• Argue to and persuade customers, teachers and a board both orally and in writing
• Use several methods of written and graphic communication: text, tables, equations, sketches, maps, graphs, etc.
• Present analysis or experiment results in laboratory reports
• Demonstrate thoroughness and independence throughout their studies
• Demonstrate self-awareness, asses themself, and develop appropriate learning strategies.
• Develop their scientific curiosity and open-mindedness

Learning Outcomes of UE

Use the presented approaches like guides and implement them to develop a solution to a given problem (case study) in information processing.

UE Content: description and pedagogical relevance

Digital information coding
Information analysis (clustering, principal componenent analysis)
Classification (Bayes theory, Gaussian Mixture Models)
Dynamic systems (dynamic time warping, Hidden Markov Models)
Introduction to Artificial Neural Networks and Deep Learning.

Lectures are punctuated by exercises which present concrete cases of application and/or are the object of reflection allowing critical analysis of the methods presented through question and answer exchanges.

The practical sessions are designed to develop students' understanding of the material covered in the course, as well as their critical analysis skills. It is therefore essential to be up to date with the teaching in order to benefit from these sessions.

Teaching is given in French.

Prior Experience

fundamentals in statistics