| Part I. Foundations | | |
| Coming | Lecture 1. Introduction and Review (Chapters 1–4) | Coming |
| Coming | Lecture 2. Inference about a Mean Vector (Chapter 5) | Coming |
| Part II. Dimension Reduction | | |
| Coming | Lecture 3. Principal Component Analysis (PCA) (Chapter 8) | Coming |
| Coming | Lecture 4. Factor Analysis (Chapter 9) | Coming |
| Coming | Lecture 5. Multidimensional Scaling (MDS) and Nonlinear Embedding (Chapter 12.6 + Supplement) | Coming |
| Part III. Classification and Clustering | | |
| Coming | Lecture 6. Discriminant Analysis (Chapter 11) | Coming |
| Coming | Lecture 7. Classification Extensions (Chapter 11 + Supplement) | Coming |
| Coming | Lecture 8. Cluster Analysis (Chapter 12) | Coming |
| Part IV. Regression and Canonical Methods | | |
| Coming | Lecture 9. Multivariate Regression (Chapters 6 and 7) | Coming |
| Coming | Lecture 10. Canonical Correlation Analysis (CCA) (Chapter 10) | Coming |
| Coming | Lecture 11. Correspondence Analysis (Chapter 12.7 + Supplement) | Coming |
| Part V. Advanced and Modern Topics | | |
| Coming | Lecture 12. Structural Equation Models (SEM) (Supplement) | Coming |
| Coming | Lecture 13. Partial Least Squares (PLS) (Supplement) | Coming |