The objective of this lab is to introduce Trees, Tree pruning and then boosting by use of random forests. Both classification and estimation problems are studied.
1. Basic properties and first steps with perceptrons ['N1_Perceptron.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/10_NN_MLPC/N1_Perceptron.ipynb)
2. First examples of perceptron based classifiers ['N2_MLPClassifier.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/10_NN_MLPC/N2_MLPClassifier.ipynb)
3. Example of MLP based regressors, basic porperties['N3_MLPRegressor.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/10_NN_MLPC/N3_MLPRegressor.ipynb)
4. MLP based handwritten digit classification : case study on Mnist Database ['N4_MLP_MNIST.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/10_NN_MLPC/N4_MLP_MNIST.ipynb)
5. Convolutional network based handwritten digit classification : case study on Mnist Database ['N5_CNN_MNIST.ipynb'](https://gricad-gitlab.univ-grenoble-alpes.fr/ai-courses/autonomous_systems_ml/-/blob/master/notebooks/10_NN_MLPC/N5_CNN_MNIST.ipynb)
