Before going through Unit 1, please review Unit 0. This material will not be covered in class and it is expected that if you are not already familiar with it, you will learn it by the end of the first week.
Note This schedule is subject to changes throughout the term. Please check this schedule on a weekly basis. Changes will only effect exams in rare circumstances. You should check the commit history on a weekly basis to check if any changes have been made.
| Date | Unit Material | Topics | Notes |
|---|---|---|---|
| Week 1 | |||
| Mon Sept 15 | Unit 1 | Intro, Unit 1.1 (Probability models) | |
| Wed Sept 17 | Unit 1 | Unit 1.2 (Conditional probability, marginalization) | |
| Fri Sept 19 | Unit 1 | Python, working with tabular data | Course survey |
| Week 2 | |||
| Mon Sept 22 | Unit 2 | Unit 2.1 (Expectation, conditional expectation, variance) | |
| Wed Sept 24 | Unit 2 | Unit 2.2 (Continuous distributions, Normal distribution and CLT) | I didn’t cover continious distributions in detial. This will not be so important for future sections. |
| Fri Sept 26 | Unit 3 | Unit 2.3 (linear regression with binary predictor) | I introduced the idea of sample distribution in the context of linear regression with binary predictor |
| Week 3 | |||
| Mon Sept 29 | Unit 3 | Unit 3.1 cont. (estimators, bias, consistency) Unit 3.2 (linear regression with normal predictor) | |
| Wed Oct 1 | Unit 3 | Unit 3.2 cont. (Least squares, Correlation, coefficient of determination) | |
| Fri Oct 3 | Unit 3 | Unit 3 leftover if needed + Exploratory data analysis/project discussion | End of midterm material |
| Week 4 | |||
| Mon Oct 6 | Review | Come with questions! | |
| Wed Oct 8 | MIDTERM IN CLASS | ||
| Fri Oct 10 | Work on project | project progress (on canvas) | |
| Week 5 | |||
| Mon Oct 13 | Unit 4 | Unit 4.1 (Multiple predictor regression examples, interpreting regression coefficients) | |
| Wed Oct 15 | Unit 4 | Unit 4.2 (Simpsons paradox, effects of adding predictors) | |
| Fri Oct 17 | Unit 4 | Unit 4 (colinearity, the joint sample distribution) | |
| Week 6 | |||
| Mon Oct 20 | Unit 4 | Unit 4 (models with catagorical predictors) | |
| Wed Oct 22 | Unit 4 | Unit 4 (analysis of variance) | |
| Fri Oct 24 | Unit 5 | Unit 5 (interactions, residual plots) | |
| Week 7 | |||
| Mon Oct 27 | Unit 5 | Unit 5 (polynomial regression, linear regression view of fourier series ) | |
| Wed Oct 29 | Unit 5 | Unit 5(bias-variance trafeoff revisited and overfitting) | |
| Fri Oct 31 | Unit 6 | Unit 6.3 (overparamaterized models, ridge and lasso regression) | |
| Week 8 | |||
| Mon Nov 3 | Unit 6 | Unit 6 (bayesian inference for bernoulli model, beta distribution laplace rule of succession) | |
| Wed Nov 5 | Unit 6 | Unit 6 (bayesian linear regression) | |
| Fri Nov 7 | Unit 6 | Unit 6 (connection between bayesian inference and regularization) | Last day of final material |
| Week 9 | |||
| Mon Nov 10 | Unit 7 | statistics vs. machine learning, the kernel trick | |
| Wed Nov 12 | Unit 7 | Gaussian processes as a Bayesian forier model | |
| Fri Nov 14 | Unit 7 | Logistic regression, project | |
| Week 1 | |||
| Mon Nov 17 | Review |