Bachelor Courses
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- Apprentissage par réseaux de neurones (ARN, 2022)
- 1. Machine Learning basics (reminder)
- 2. Perceptrons
- 3. Multi-Layer Perceptrons (MLP)
- 4. Neural Networks’ training
- 5. Neural Networks’ monitoring
- 6. Convolutional Neural Networks (CNN)
- 7. From Shallow to Deep Neural Networks
- 8. Convolutional Neural Network Architectures
- 9. Transfer Learning, Embeddings and Meta-Learning
- 10. Deep troubles
- 11. Deep Learning Applications
- 12. Beyond Convolutional Neural Networks
- Introduction à la science des données (ISD, 2021)
- 1. Outils pour la modélisation data-driven
- 2. Introduction à la science des données
- 3. Apprentissage automatique (Machine Learning)
- 4. Bibliothèques pour le calcul scientifique
- 5. Analyse exploratoire des données
- 6. Apprentissage supervisé
- 7. Evaluation des modèles
- 8. Apprentissage supervisé 2: algorithme LVQ
- 9. Régression linéaire
- 10. Données et caractéristiques
- 11. Apprentissage non-supervisé
- 12. Conclusions et perspectives
- Apprentissage par réseaux de neurones (ARN, 2022)
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- Architecture des ordinateurs (ARO1, 2003-2018)
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- Développement de dispositifs médicaux (DDM, 2019)
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- Interfaces Homme-Machine (IHM, 2018-2022)
- Machine Intelligence (2008 & 2010)
- Machine Learning (MLG 2016-2020)
- 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)