Machine-learning-notes

HKU COMP3314 notes, comments on Python Machine Learning (2nd edition)
Ch02: perceptron, adaline, stochastic gradient descent
Ch03: logistic regression, SVM, kernel SVM, decision tree, k-nearst neighbours
Ch04: NaN, data preprocessing, data split, regularization, feature importance with random forest
Ch05: dimensionality reduction, PCA, LDA, kernal PCA
Ch06: model evaluation, hyperparameter tuning, k-fold cv, grid search
Ch07: majority voting, bagging, boosting
Ch12: artificial neural network