Work from course at mcgill which is an introduction into machine learnign methods. Coverered
Linear regression. Linear classification. Performance evaluation, overfitting, cross-validation, bias-variance analysis, error estimation. Naive Bayes. Decision trees. Regression trees and ensemble methods. Cost-sensitive learning. Support vector machines. Artificial neural networks. Deep learning. Feature selection. Dimensionality reduction. Regularization. Unsupervised learning and clustering.