Teaching (2022 - 2023)
- Master 1: Agility
- Lecture: PDF file
- Pratical works:
- TP0: Git introduction
- TP1: Continuous integration
- TP2: Forest simulator (unit tests)
- TP3: Forest simulator (TDD)
- TP4: Forest simulator (TDD + quality metrics)
- TP5: Interface testing (available later…)
- TP6: Design by contract (available later…)
- Project: final project (available later…)
- Master 1: Machine learning
- Lecture: PDF file
- Tutorials:
- TD1: Decision tree (available later…)
- TD2: Perceptron (available later…)
- Pratical works:
- TP0: Python for ML introduction
- TP1: Linear and Polynomial regression
- TP2: KNN (available later…)
- TP4: K-means (available later…)
- TP5: Upper Confidence Bound (available later…)
- TP6: Q-learning (available later…)
- TP7: Neural Network (available later…)
- TP8: Naive Bayesian classification (planned but TBC)
- Projects:
- Intermediate: supervised learning (available later…)
- Final project: advanced ML problem (available later…)
- Other resources:
- Configure Python: virtual environement
- Python: OOP and library main concepts