Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.
Explores decision and regression trees, impurity measures, learning algorithms, and implementations, including conditional inference trees and tree pruning.
Covers ensembling, bagging, random forests, variable importance, and OOB cross-validation in machine learning.
Covers boosting with a focus on the Adaboost algorithm, forward stagewise additive modeling, and gradient tree boosting.
Introduces the k-means algorithm for clustering data points based on centroids and iterative updates.
Covers the training of neural networks using stochastic gradient descent, chain rules, gradients computation, weight decay, and dropout.
Explores convolutional neural networks, covering convolution, cross-correlation, max pooling, layer structure, and examples like LeNet5 and AlexNet.
