Objectives of Programme's Learning Outcomes

• Imagine, implement and operate systems/solutions/software to address a complex problem in the field of electrical engineering as a source of information by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Identify complex problems to be solved and formulate the specifications by integrating client needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Based on modelling and experimentation, design one or more systems/solutions/software addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Implement a chosen system/solution/software in the form of a drawing, a schema, a flowchart, an algorithm, a plan, a model, a prototype, software and/or digital model.
• Evaluate the approach and results for their adaptation (tests, measurements, optimisation and quality).
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out electrical engineering missions, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques specific to electrical engineering.
• Analyse and model a problem by critically selecting theories and methodological approaches (modelling, calculations), and taking into account multidisciplinary aspects.
• Identify and discuss possible applications of new and emerging technologies in the field of electrical engineering.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.
• Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
• Master technical English in the field of electrical engineering.

Learning Outcomes of UE

develop an applied pattern recognition system, together with a critical analysis of the problem;
apply image analysis and segmentation techniques
apply data processing techniques (feature extraction, feature selection);
apply classification and machine learning techniques (Gaussian models, Clustering, Artificial Neural Networks, Dynamic Time Warping, Hidden Markov Models, Deep Neural Networks);
estimate performances of classifiers.

Content of UE

Image Processing: Image acquisition; lowlevel processing, filtering, transforms; image segmentation and registration;
Pattern Recognition: SPR scheme, feature extraction, classifiers, combining classifiers; neural networks:feed-forward neural networks, training MLP, Deep Neural Nets; support vector machines; dynamic systems: dynamic time warping, hidden Markov models

Prior Experience

fundamentals of signal processing; probability and statistics

Type of Assessment for UE in Q1

• Oral examination

Q1 UE Assessment Comments

Evaluation methods may be adjusted according to the teaching/evaluation context imposed by health measures.

Type of Assessment for UE in Q3

• Oral examination

Q3 UE Assessment Comments

Evaluation methods may be adjusted according to the teaching/evaluation context imposed by health measures.

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable