Objectives of Programme's Learning Outcomes

• 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).
• Identify complex problems to be solved and develop the specifications with the client by integrating needs, contexts and issues (technical, economic, societal, ethical and environmental).
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
• 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.
• 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.
• Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
• 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 UE, the student should have acquired theoretical knowledge and practical skills related to one of the major paradigms of AI: probabilistic models (probabilistic reasoning). He/she should :
- understand the importance and interest of the probabilistic perspective in AI.
- know the basic theory of probabilistic graphical models and Bayesian networks.
- know the hidden Markov models, particle filters, mixtures of Gaussians, the latent Dirichlet allocation, linear and non-linear Bayesian models.
- be able to put into practice statistical inference approaches.
- know how to use software libraries dedicated to probabilistic programming (PyMC, Pyro, etc.).
 

UE Content: description and pedagogical relevance

The UE is composed of one learning activities that exposes:
- artificial intelligence relying on probability theory, based on probabilistic graphical models, Bayesian networks, and their implementation through probabilistic programming.

This learning activity will include practicals to acquire the theory.

More details about the content are provided in the ECTS sheet of the learning activity.

Prior Experience

Not applicable