In this chapter, we discussed a few classical machine learning techniques and their applications in solving image processing problems. We started with unsupervised machine learning algorithms such as clustering and principal component analysis. We demonstrated k-means and spectral clustering algorithms with scikit-learn and showed you how they can be used in vector quantization and segmentation. Next, we saw how PCA can be used in dimension reduction and the visualization of high-dimensional datasets such as the scikit-learn handwritten digits images dataset. Also, how the PCA can be used to implement eigenfaces using a scikit-learn face dataset was illustrated.
Then, we discussed a few supervised machine learning classification models, ...
