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.
• 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.
• Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Deliver a solution selected in the form of diagrams, graphs, prototypes, software and/or digital models.
• Evaluate the approach and results for their adaptation (modularity, optimisation, quality, robustness, reliability, upgradeability, etc.).
• Integrate technological innovation and intelligence within engineering teams.
• Work effectively in teams, develop leadership, and make decisions in multidisciplinary, multicultural and international contexts.
• Make decisions, individually or collectively, taking into account the parameters involved (human, technical, economic, societal, ethical and environmental).
• 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.
• Argue to and persuade customers, teachers and boards, both orally and in writing.
• Select and use the written and oral communication methods and materials adapted to the intended purpose and the relevant public.
• Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
• Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
• Show an open and critical mind by bringing to light technical and non-technical issues of analysed problems and proposed solutions.
• Exploit the different means available in order to inform and train independently.
• Contribute by researching the innovative solution of a problem in engineering sciences.
• Construct a theoretical or conceptual reference framework, formulate innovative solutions from the analysis of scientific literature, particularly in new or emerging disciplines.
• Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
• Collect and analyse data rigorously.
• Adequately interpret results taking into account the reference framework within which the research was developed.
• Communicate, in writing and orally, on the approach and its results in highlighting both the scientific criteria of the research conducted and the theoretical and technical innovation potential, as well as possible non-technical issues.

Learning Outcomes of UE

At the end of this course, the student should have acquired theoretical knowledge and practical skills related to random and probabilistic models for operational research and artificial intelligence. He/she should:
- understand the importance and interest of this perspective.
- know the basic theory of probabilistic graphical models and Bayesian networks.
- know various models: Markov chains, hidden Markov models, mixtures of Gaussians, latent Dirichlet allocation, linear and non-linear Bayesian models.
- be able to implement inference approaches.
- know how to use the dedicated software libraries.
The EU will also cover:
- Model a system using a Markov chain and determine its behavior
- propose a solution to deal with a queuing system
- Be able to use matrix factorization for unsupervised data analysis

 

UE Content: description and pedagogical relevance

The UE is made up of two AAs:
- one that presents and analyzes different random models for operational research, focusing mainly on Markov chains and their applications (Google PageRank, queues, etc.). He will also study matrix factorization in the context of unsupervised learning.
- the other which presents other Bayesian models, and their implementation via probabilistic programming and inference. This AA presents hidden Markov models, mixtures of Gaussians, latent Dirichlet allocation, and linear and non-linear Bayesian models.

More details on the content are given in the ECTS files of these AAs.
 

Prior Experience

Not applicable