This is the page for the course Statistical Machine Learning (ENGG-6600-02), offered in Summer 2023 at the University of Guelph.
Videos of Lectures:
See videos on Youtube
Lectures
- Course introduction:
- Overfitting, cross validation, regularization: (slides), (annotated slides)
- Linear discriminant analysis (LDA) and Quadratic discriminant analysis (QDA): (slides), (annotated slides)
- Linear regression, ridge regression, Lasso regression: (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)
- Multidimensional scaling (MDS), Sammon mapping, Isomap: (slides), (annotated slides)
- Locally linear embedding (LLE): (slides), (annotated slides)
- Variational inference, factor analysis, probabilistic PCA: (slides), (annotated slides)
- Stochastic neighbor embedding (SNE) and t-SNE: (slides), (annotated slides)
- Uniform Manifold Approximation and Projection (UMAP): (slides), (annotated slides)
- Hidden Markov model (HMM): (slides), (annotated slides)