Statistical Machine Learning (ENGG-6600) - University of Guelph

This is the page for the course Statistical Machine Learning (ENGG-6600-08), offered in Fall 2023 at the University of Guelph.

Videos of Lectures:

See videos on Youtube

Lectures

• Course introduction:
  • Course outline: (outline)
  • Course introduction: (slides), (annotated slides)
  • Preliminaries: (slides), (annotated slides)
• Overfitting, cross validation, regularization: (slides), (annotated slides)
• Linear regression, ridge regression, Lasso regression: (slides), (annotated slides)
• Linear discriminant analysis (LDA) and Quadratic discriminant analysis (QDA): (slides), (annotated slides)
• Support vector machine (SVM) and kernel SVM: (slides), (annotated slides)
• K-nearest neighbors (KNN): (slides), (annotated slides)
• Bayes and Naive Bayes classifiers: (slides), (annotated slides)
• Logistic regression: (slides), (annotated slides)
• Tree and random forest: (slides), (annotated slides)
• Boosting and AdaBoost: (slides), (annotated slides)
• Mixture distribution and Gaussian mixture model: (slides), (annotated slides)
• Principal component analysis (PCA), dual PCA, kernel PCA, supervised PCA: (slides), (annotated slides)
• Fisher discriminant analysis (FDA), kernel FDA: (slides), (annotated slides)
• Variational inference, factor analysis, probabilistic PCA: (slides), (annotated slides)
• Stochastic neighbor embedding (SNE) and t-SNE: (slides), (annotated slides)