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
Bias-variance decomposition
Sep 8 linear regression overview Linear regression
Sep 13 interpreting regression coefficients Linear regression
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
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
ISLR S.4.1–3
Nov 17 generalized linear models
linear discriminant analysis
Nov 17 Scribble
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 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

Other references

Subject References
Linear algebra Linear algebra review and reference
Probability Review of probability theory
The multivariate Gaussian distribution
More on multivariate Gaussians