IGSE SIC 7002
Master of Science
This course will help students to increase their knowledge in the field of Artificial Intelligence (AI) for data science and to have an insight on applied research in this field.
At the end of this module, the students should be able to:
- Master the fundamental concepts of Artificial Intelligence (AI)
- Describe and explain the automatic data processing chain in a real operational system: multimedia data acquisition with sensors, data pre-processing, feature extraction, match between test and reference data, artificial learning, and decision-aid for automatic classification of data.
- Apply techniques of AI to different problems: signal and image processing, pattern recognition, data mining, machine learning, classification, decision-aid.
- Evaluate the pertinence of these techniques within the framework of a specific application.
- 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
- Identify the companies who develop these techniques as part of their professional applications or consumer products.
- Enounce the scientific advances in AI and the changes in customer experience caused by AI.
- Presentations performed by Industrial actors particularly in the areas of Biometrics, Telemedicine, Data Security and Neuroscience.
- Conferences on current applications in such areas by End-users, public or private Organisations (Governments, Airports among others) and Hospitals (AP-HP, SAMU-92, CHU-Toulouse…).
- Introduction to market trends concerning these ICT applications in the areas of security and Telemedicine.
- Ethical and legal aspects: International and European legislations (personal data protection).
- Pattern recognition techniques, knowledge in signal and image processing
Operational system, Signal processing, Image processing, Pattern Recognition, Global Systemic Approach, Embedded processing, Security, Systems Robustness.
Mark 1 = written evaluation
Mark 2 = oral presentation
Final mark = 50 % Mark 1 + 50 % Mark 2