Objectives of Programme's Learning Outcomes

• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, with a focus on Innovation and Information Systems, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques related to the improvement of decision and management processes, mastery of mathematical modelling and optimisation algorithms, analysis of large volumes of data, mastery of Web and multimedia tools, design and operation of distributed and mobile computing systems, management of a software project, innovative management of a company and/or project team, information systems (data mining, database, cloud computing, etc.) and management of technological innovation.
• Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.
• Identify and discuss possible applications of new and emerging technologies in the field of information technology and sciences and quantifying and qualifying business management.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.

Learning Outcomes of UE

AA "Intelligence Artificielle": at the end of this course, the student will be able to:
- manage, formulate and solve problems in artificial intelligence ;
- master the concepts of artificial intelligence: agents and environment, multi-agent systems, neural networks, machine learning, etc.
- understand the connection between Artificial Intelligence, Data Science, Machine and Deep Learning.

At the end of the class the student will master the fundamentals of the study of univariate time series, which amounts to
- understand and explain a few models in depth;
- get acquainted to the use of a statistical software in view of fitting a model to a time series and using it for forecasting purposes;
- be able to assess the model and the precision of the forecasts.

Content of UE

AA "Intelligence Artificielle":
- Introduction and definition of artificial intelligence;
- Intelligent agents;
- Multi-agent systems;
- Recall and terminology of machine learning;
- Deep neural networks (Deep Learning);
- Types of deep neural networks (MLP, CNN, RNN, etc.).

AA "Streaming Data Analysis" :
- univariate time series (decomposition: trend, seasonality, cyclic component);
- ARIMA models and Box-Jenkins methodology;
- exponential smoothing;
- overview of multivariate time series.

Prior Experience

Not applicable

Type of Assessment for UE in Q1

• Written examination
• Practical test

Q1 UE Assessment Comments

AA "Intelligence Artificielle": written exam + practical work on computer
AA "Streaming Data Analysis" : written exam + practical work on computer

Type of Assessment for UE in Q3

• Written examination
• Practical Test

Q3 UE Assessment Comments

Idem Q1

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable