School of Data Science
University of Virginia
Course Overview
Course Calendar
Course Policies
View the Project on GitHub thomasgstewart/machinelearning1fall2023
The final project presentations will be Saturday, December 9, 2023 from 9:00AM12:00PM.
Title  First Submission Due Date  Resubmission Due Date 

1. Student Profile (not graded)  20230828  Not available 
2. Linear independence  20230904  
3. Appendix A Exercises  20230908  
4. Nonlinear  20231002  
5. PCA  20231018  
6. Constrained kernel  20231018  
7. Optimism  20231205  
8. Ordinal regression model  20231205  
9. Logistic regression model  20231205 
Material in monospaced font
are in the course Teams site. The letter H denotes sections in Regression Modeling Strategies; the letter C denotes sections in Plane Answers.
Class date  Topic  Material 

Data types  01datatypes 

Course overview  
20230828  Linear Algebra Review  C Appendix A, C Appendix B, C 1 
→ Vector space  
→ Vector subspace  
→ Matrix as a function  
→ Span  
→ Column space  
→ Linear dependence  
→ Basis  
→ Rank  
→→ Uniqueness  
→ Sum of subspaces  
→→ Uniqueness  
→ Orthogonal vectors  
→ Orthogonal basis  
→ Orthonormal basis  
→ GramSchmidt  
→ Orthogonal complement  
→→ Decomposition of vector space into subspace and orthogonal complement  
Linear Regression  
Model formulation  Slides in Teams  
Bayesian vs MLE  Slides in Teams  
Interactions  Slides in Teams  
Nonlinearity  Slides in Teams  
Measures of model performance  
→ Discrimination  
→ Calibration  
→ Optimism  
Measures of model performance  
Carrying capacity of data  
Model complexity  
Strategies for rightsizing the model complexity  
→ regularization (LASSO, ridge, Bayesian)  
→ constraints (principle components, monotonicity)  
Logistic Regression  
Model formulation  
Bayesian vs MLE  
Interactions  
Nonlinearity  
Measures of model performance  
→ Discrimination  
→ Calibration  
→ Optimism  
Measures of model performance  
Carrying capacity of data  
Model complexity  
Strategies for rightsizing the model complexity  
→ regularization (LASSO, ridge, Bayesian)  
→ constraints (principle components, monotonicity)  
Ordinal Regression  
Model formulation  
Bayesian vs MLE  
Interactions  
Nonlinearity  
Measures of model performance  
→ Discrimination  
→ Calibration  
→ Optimism  
Measures of model performance  
Carrying capacity of data  
Model complexity  
Strategies for rightsizing the model complexity  
→ regularization (LASSO, ridge, Bayesian)  
→ constraints (principle components, monotonicity)  
Hazard Regression  
Model formulation  
Bayesian vs MLE  
Interactions  
Nonlinearity  
Measures of model performance  
→ Discrimination  
→ Calibration  
→ Optimism  
Measures of model performance  
Carrying capacity of data  
Model complexity  
Strategies for rightsizing the model complexity  
→ regularization (LASSO, ridge, Bayesian)  
→ constraints (principle components, monotonicity)  
Random Forest  
Model formulation  
Bayesian vs MLE  
Interactions  
Nonlinearity  
Measures of model performance  
→ Discrimination  
→ Calibration  
→ Optimism  
Measures of model performance  
Carrying capacity of data  
Model complexity  
Strategies for rightsizing the model complexity  
→ regularization (LASSO, ridge, Bayesian)  
→ constraints (principle components, monotonicity) 