Teaching

Bachelor Courses

  • Machine Intelligence (MIN 2023-)
    • 01. Introduction: from GOFAI to modern AI
    • 02. Self-supervised learning
    • 03. Generative Adversarial Networks
    • 04. Machine Learning and creativity
    • 05. Reinforcement Learning
    • 06. Deep Reinforcement Learning
    • 07. Reinforcement Learning applications
    • 08. Artificial Evolution
    • 09. Embodied Cognition
    • 10. From Collective Intelligence to Machivellian Intelligence
    • 11. Agent-based models, Artificial Life and Complexity
    • 12. Agent-based model applications
  • MAGICIEL:MAtériel et LoGICIEL des ordinateurs (MAG, 2004-2014)
  • Science-fiction et technologie (SFI, 2015)
  • Systèmes bio-inspirés (SBI, 2005-2015)
    • Master Courses

      • Machine Learning (TSM_MachLe, 2017-)
        • 9. Artificial Neural Networks
        • 10. Deep Learning & Convolutional Neural Networks
        • 12. Autoencoders
        • 13. Recurrent Neural Networks
        • 14. Dimensionality reduction
      • Machine Learning on Big Data (MLBD 2016-)
        • 1. Introduction
        • 2. Image processing using Convolutional Neural Networks
        • 3. Remote Sensing: a Big Data case study
        • 4. Change and Anomaly detection in Big Spatiotemporal Data.
      • Quantified Self (QSelf, 2018-2021)
        • 1. Introduction to Quantified Self
        • 2. Sensors for Quantified Self
        • 3. Sensor data for Quantified Self
        • 4. Quantified Self: physical state monitoring
        • 5. Quantified Self: cognitive state monitoring
      • AI for Games and Simulation (AIGS, 2012 & 2013)
      • Smart Devices and Applications (SDA, 2010 & 2011)