School of Data Science
University of Virginia
Course Overview
Course Calendar
Course Policies
View the Project on GitHub thomasgstewart/machine-learning-1-fall-2022
The final project presentations will be Thursday, December 8, 2022 from 2:00PM-5:00PM.
| Title | First Submission Due Date | Resubmission Due Date |
|---|---|---|
| 0. Student Profile | 2021-08-29 | Not available |
| 1. Facial metrics | 2022-09-16 | 2022-10-31 |
| 2A. Framingham Heart Study | 2022-09-26 | 2022-10-31 |
| 2B. Framingham Heart Study Diagnostics | 2022-10-12 | 2022-10-31 |
| 3. PCA | 2022-11-04 | |
| 4. Case study | 2022-11-16 | |
| 5. Stroke | 2022-11-28 | |
| 6. | ||
| 7. | ||
| 8. | ||
| 9. |
Material in monospaced font are in the course Teams site. The letter H denotes sections with the course text book.
| Class date | Topic | Material |
|---|---|---|
| 2022-08-26 | Data types | 01-data-types |
| 2022-08-29 | Course overview | 02-course-outline |
| Linear Regression | ||
| Model formulation | lion-age-nose, H 2.2 |
|
| Bayesian vs MLE | lion-age-nose-bayesian Maximum Likelihood and Bayesian Analysis salary-bayes.R salary-mle.R |
|
| Interactions | 2.3 | |
| Nonlinearity | H 2.4 - 2.5 | |
| Measures of model performance | H 5 | |
| → Discrimination | ||
| → Calibration | ||
| → Optimism | ||
| Measures of model performance | H 5 | |
| Carrying capacity of data | ||
| Model complexity | ||
| Strategies for right-sizing 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 right-sizing 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 right-sizing 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 right-sizing 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 right-sizing the model complexity | ||
| → regularization (LASSO, ridge, Bayesian) | ||
| → constraints (principle components, monotonicity) |