Teaching

Bachelor Courses

• • Architecture des ordinateurs I
    • Last practical work: Machines d’états et le robot Thymio
  • Interfaces Homme-Machine (IHM, 2018)
    • 0. Introduction
    • 1. Design of interfaces: basic concepts
    • 2. The human in the loop
      • Labo Interface pour FFPMEG [Qt basics / IHM_FFMPEG] (delay 05.11.18)
    • 3. Context-aware interfaces
    • 4. Emerging HCI
      • Labo Introduction à Kivy (delay 09.12.18)
    • 5. Data visualization
  • Homeworks
  • Mini-project: Labo Autonomous self-driving car dashboards
  • Reading: HCI & AI joining forces

• Machine Intelligence (2008 & 2010)
• Machine Learning (MLG 2018)
  • 01. Introduction
    • Lab1: Introduction to Python notebooks (zip, pdf)
  • 02. Artificial Neural Networks
    • Lab2: Perceptrons, MLPs and Backpropagation
  • 03. Artificial Neural Networks II
    • Lab3: Crossvalidation
  • 04. Feature engineering
    • Lab4: Model selection
    • Lab5: Speaker recognition (delay for report: < X.4.2018, 23h59)
  • 05. Deep Learning
    • Lab6: CNNs
  • 06. Unsupervised learning
    • LabSOM_Part1.zip
    • LabSOM_Part2.zip (delay for report: < X.5.2018, 23h59)
  • 07. Reinforcement Learning
    • Introductory chapter (PDF)
  • 08. Genetic Algorithms
    • GAlab1: design with a purpose
    • GAlab2: a tool for optimization (delay for report < X.6.2018, 23h59)
  • 09. Swarm Intelligence
  • 10. Artificial Intelligence
• MAGICIEL:MAtériel et LoGICIEL des ordinateurs (MAG, 2004-2014)
• Science-fiction et technologie (SFI, 2015)
• Systèmes bio-inspirés (SBI, 2005-2015) 

Master Courses

• Quantified Self (QSelf, 2018)
  • Moodle MSE
  • 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
• Machine Learning (TSM_MachLe, 2017)
  • Moodle MSE
  • 6. Artificial Neural Networks
  • 7. Deep Learning & Convolutional Neural Networks
  • 9. Recurrent Neural Networks
  • 10. Dimensionality reduction
  • 11. Reinforcement Learning
  • 14. Final feedback
• Machine Learning on Big Data (MLBD 2018)
  • Moodle MSE
  • 1. Introduction
  • 2. Feature engineering
  • 3. Feature construction
  • 4-9. Fuzzy Modeling and feature selection in very wide databases (by C. Peña)
  • 10. Anomaly detection in very large databases
• Machine Learning on Big Data (MLBD 2016)
  • 1. Introduction
  • 2. What is Learning (by C. Peña)
  • 3. Hands-on learning
  • 4. Supervised Learning and Artificial Neural Networks
  • 5. Evolutionary Fuzzy Modelling (by C. Peña)
  • 6. Feature and Model selection (by C. Peña)
  • 7. Unsupervised Learning
  • 8. Reinforcement Learning
  • 9. Convolutional Neural Nets
  • 10. AlphaGO
  • 11. Deep Learning on GPUs (by J. Rebetez)
• AI for Games and Simulation (AIGS, 2012 & 2013)
  • 0. Introduction
  • 1. AI & Games
  • 2. GOFAI
  • 3. Bio-inspired systems
  • 4. Supervised learning
  • 5. Reinforcement learning
  • 6. Simulation
  • 7. Games for a better world
• Smart Devices and Applications (SDA, 2010 & 2011)