Artificial intelligence for data science

Learning objectives

- To acquire a global systemic view of automatic data processing, across the different steps composing any real operational system: data acquisition via sensors, data pre-processing, feature extraction, data modelling, match of sensed data to reference data stored in a large database, use of decision parameters for data final classification;
- To apprehend the systemic vision regarding adverse conditions in case of real operational use of a system: system robustness to noise, system robustness to attacks, system time response, system reliability;
- To apprehend the systemic vision for access control and its security (Smart card, centralized servers, transactions security, biometric data protection);
- To know the main applications of concepts and tools of Pattern Recognition, Multimedia Signal acquisition, Multimodal Fusion and Decision assistance;
- To acquire knowledge of the industrial actors who develop these techniques in the frame of their professional applications or their mass-market products;
- To measure the benefits and limits of these techniques through their current industrial applications;
- To know end-user needs and constraints (e-government, public organisations, hospitals, etc.) interested by future application of these systems, but also their current solutions.