Commit 987361fe authored by Olivier Michel's avatar Olivier Michel 💬
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# Lab 7 statement
The objective of this lab is to illustrate basic concepts of clustering.
_Note: For each notebook, read the cells and run the code, then follow the instructions/questions in the questions` or `Exercise` cells._`
See the notebooks in the [`7_Clustering`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/) folder
1. Implement your own version of Kmeans in 1D for Euclidean distance [`N1_Kmeans_basic.ipynb`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/N1_Kmeans_basic.ipynb)
and compare the obtained results with Sklearn Kmeans implementation.
2. Apply Kmeans algorithm (Sklearn implementation) to the classical Iris dataset [`N2_KMeans_iris_data_example.ipynb`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/N2_KMeans_iris_data_example.ipynb)
3. Implement a Kernelized version of Kmeans, and test the importance of adequate parametrization or choice of initial conditions
[`N3_Kernel_Kmeans_example.ipynb`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/N3_Kernel_Kmeans_example.ipynb)
4. Implement your own version of EM for Gaussian model and apply it to the same example used for Kmeans in a preceeding notebook. Compare with KMeans and interpret the results [`N4_EM_basic.ipynb`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/N4_EM_basic.ipynb)
5. Example of EM application on the Iris data set [`N5_EM_iris_data_example.ipynb`](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/notebooks/7_Clustering/N5_EM_iris_data_example.ipynb)
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