Bachelor Courses
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- Architecture des ordinateurs I
- Last practical work: Machines d’états et le robot Thymio
- Développement de dispositifs médicaux (DDM, 2019)
- 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
- Architecture des ordinateurs I
- Machine Intelligence (2008 & 2010)
- Machine Learning (MLG 2018)
- 01. Introduction
- 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)
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- Science-fiction et technologie (SFI, 2015)
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- 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)