Objectives of Programme's Learning Outcomes

• Implement an engineering approach dealing with a set problem taking into account technical, economic and environmental constraints
• Identify and describe the problem to be solved and the functional need (of prospective clients) to be met considering the state of technology
• 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
• Communicate in a structured way - both orally and in writing, in French and English - giving clear, accurate, reasoned information
• Present analysis or experiment results in laboratory reports
• Demonstrate thoroughness and independence throughout their studies
• Identify the different fields and participants in engineering
• Develop their scientific curiosity and open-mindedness
• Learn to use various resources made available to inform and train independently

Learning Outcomes of UE

- analyze various kinds of data and signals - understand the underlying theory for the development of basic components of a numerical signal processing system - implement these components with MATLAB - understand and explain the theory, models and techniques used for statistical data analysis - analyse datasets with a given software (Python, MATLAB, R, Weka, ...) - interpret the results from the software, showing an understanding of the theory

UE Content: description and pedagogical relevance

- linear and invariant numerical systems; frequency analysis of numerical signals and systems; Shannon theorem and sampling; discrete Fourier transform; spectral analysis of random signals; numerical filters; simple systems under MATLAB - descriptive techniques for data analysis (principal components analysis, discriminant factorial analysis) - classical models of data analysis (analysis of variance, linear regression) - data mining / machine learning (classification and clustering)

Prior Experience

Algebra, Analysis, probability and statistics, functions of complex variables