| Date | Topics | References |
|---|---|---|
| Aug 30 | course overview supervised learning |
Course overview Supervised learning ISLR Ch 2†; Optimality of conditional expectation |
| Sep 1 | bias-variance trade-off | Supervised learning ISLR Ch 2 Bias-variance decomposition |
| Sep 8 | linear regression overview | Linear regression ISLR Ch 3 |
| Sep 13 | interpreting regression coefficients | Linear regression ISLR Ch 3 |
| Sep 15 | ordinary least squares (OLS) | Sep 15 Scribble OLS notes S.1‡ |
| Sep 20 | linear model + Gaussian/normal error terms maximum likelihood under normality |
Sep 20 Scribble OLS notes S.2.1 |
| Sep 22 | inference under normality | Sep 22 Scribble OLS notes S.2.2 |
| Sep 27 | \(p\)-values classical linear model |
Sep 27 Scribble OLS notes S.3.1 |
| Sep 29 | method of moments under exogeneity properties of OLS under exogeneity |
Sep 29 Scribble OLS notes S.3.2–3 |
| Oct 4 | more properties of OLS under exogeneity | Oct 4 Scribble OLS notes S.3.3 |
| Oct 6 | Gauss-Markov theorem | Oct 6 Scribble OLS notes S.3.3 |
| Oct 11 | estimating the error variance \(t\)-statistic |
Oct 11 Scribble OLS notes S.3.3 |
| Oct 13 | MIDTERM | |
| Oct 20 | convergence of random variables (weak) laws of large numbers |
Oct 20 Scribble OLS notes S.B Convergence of random variables |
| Oct 25 | central limit theorems convergence of functions of random variables |
Oct 25 Scribble OLS notes S.B |
| Oct 27 | asymptotic properties of OLS | Oct 27 Scribble OLS notes S.4.1–2 |
| Nov 1 | testing statistical hypotheses | Nov 1 video lecture OLS notes S.5.1 |
| Nov 3 | \(z\)-test asymptotic power |
Nov 3 video lecture Nov 3 Scribble OLS notes S.5.2 |
| Nov 8 | estimating \(\textrm{Avar}\big[\widehat{\beta}_n\big]\) | Nov 8 Scribble OLS notes S.5.3 Estimating \(\textrm{Avar}\big[\widehat{\beta}_n\big]\) |
| Nov 10 | Wald test optimal linear prediction |
Nov 10 Scribble OLS notes S.5.4–5 |
| Nov 15 | classification overview logistic regression |
Classification ISLR S.4.1–3 |
| Nov 17 | generalized linear models linear discriminant analysis |
Nov 17 Scribble Classification ISLR S.4.4, S.4.6 |
| Nov 22 | generative models for classification | Classification ISLR S.4.4–5 |
| Nov 29 | (cross-)validation | Nov 29 video lecture Resampling methods ISLR S.5.1 |
| Dec 1 | bootstrap | Resampling methods ISLR S.5.2 |
| Dec 6, 8 | linear model selection and regularization | Linear model selection and regularization ISLR S.6 |
†ISLR refers to the textbook Introduction to Statistical Learning.
‡OLS notes refers to the notes on ordinary least squares.
| Date | Topics | References |
|---|---|---|
| Sep 2 | R tutorial | ISLR S.2.3† |
| Sep 9 | linear regression lab | ISLR S.3.6 |
| Sep 16 | interpreting regression coefficients | ISLR S.3.6 |
| Sep 23, Sep 30 | returns to scale in the US electric power industry | nerlove.XLS Returns to scale Nerlove (1957) - Returns to Scale in Electricity Supply |
| Oct 7 | midterm review | Practice midterm Practice midterm solutions |
| Oct 14 | NO LAB | |
| Oct 21 | midterm debrief | |
| Oct 28, Nov 4 | Testing the efficient market hypothesis | mishkin.XLS Efficient markets Mishkin (1991) - Is the Fisher effect real? |
| Nov 11 | Chow test for structural change | |
| Nov 18 | Classification methods lab | ISLR S.4.7 |
| Dec 2 | Resampling methods lab | ISLR S.5.3 |
| Dec 9 | final review | Practice final Practice final solutions |
| Subject | References |
|---|---|
| Linear algebra | Linear algebra review and reference |
| Probability | Review of probability theory The multivariate Gaussian distribution More on multivariate Gaussians |