"Intelligent Systems" track list of units

Semester 1

SI

• SI/S1

  • SI/S1/Compulsory

    • AI Fundamentals
    • Automatic control: frequency domain analysis
    • Information theory
    • Introduction to Machine Learning
    • Introduction to image processing
    • Introduction to sound processing
    • Object-oriented programming in Python
    • Optimisation
    • Random signals processing

Semester 2

The program outlined below is supplemented by:

• training in career guidance and professional integration (OIP), organized as a one-week full-time course at the start of the semester (3 ECTS),
• an internship in a laboratory or company (6 ECTS) that may begin at the very end of Semester 2 (by mid-May at the latest).

SI

• SI/S2

  • SI/S2/Compulsory

    • C++ Object Programming
    • Computer vision
    • Deep Learning and introduction to Reinforcement Learning
    • Natural language processing
    • ROS and experimental robotics
    • Time-frequency analysis and spatialized sound

Semester 3

The program outlined below is supplemented by:

• an "Engineering Projects" course unit (6 ECTS), in which students from the AR Master degree are grouped into multidisciplinary teams to address all aspects (management, technical, and scientific) of a major project;
• a "Scientific Lectures" course unit (3 ECTS), featuring lectures given throughout the semester by industry professionals, researchers, doctoral candidates, etc.

In addition to the coursework taken by all students in the AR Master degree, the third semester of the SI track is divided into compulsory courses (totaling 12 ECTS) and elective courses (9 ECTS). Not all elective combinations are permitted; the rules governing course selection are specified at the start of each academic year.

SI

• SI/S3

  • SI/S3/Compulsory

    • Advanced Deep Learning and Multimodal Learning
    • Advanced Image Processing
    • Machine Learning for Automatic Sound Processing

SI

• SI/S3

  • SI/S3/Elective

    • AI for Robotics
    • Deep Learning and Reinforcement Learning (refresher course)
    • Learning for Human-Robot Interaction
    • Medical Imaging
    • Multi-modal analysis of human behaviour
    • Planning and Decision-Making for Mobile Robotics
    • Teleoperation and haptic interfaces
    • Virtual Reality