Class Schedule

This page contains a schedule of the topics, content, and assignments for the semester. Note that this schedule will be updated as necessary as the semester progresses, with all changes documented here.

Instructions to save the slides as PDFs can be found here. The key is to hit E when the slides are open in your browser (ideally Chrome, but it may work in others) to toggle into PDF print mode.

Readings can be accessed using the link (when needed, through the Cornell library with a Cornell login) or on Canvas.

TipHow To Read This Schedule
  • Homework go out on the day listed and are due at 9:00pm on the Friday listed. The solution key posts one day after the due date, when the late window closes.
  • Quizzes take the first 25 minutes of the session listed. A quiz never covers material from the same week — everything through the previous Friday is fair game, and nothing after it.
  • Readings are due, with annotations and reflection, by the start of the first lecture the following week.

Assessment Schedules

The deadlines above are listed in full, with what each one covers, on their own pages:

Class-by-Class Schedule

Week Date Topic Slides Reading Homework Quiz Project
1 1/25 Class Introduction; What Is a Generative Model? HW1 out
1/27 Exploratory Data Analysis Franconeri et al. (2021)
1/29 Data Visualization
2 2/01 Probability Review HW2 out
2/03 Monte Carlo: Simulating From a Distribution HW1 due Thu 2/04
2/05 Monte Carlo Error and Convergence
3 2/08 Linear Regression Shmueli (2010) HW3 out Quiz 1
2/10 Linear Regression, Continued HW2 due Thu 2/11
2/12 The Hinge: Regression as a Probability Model
2/15 February Break
4 2/17 Named Tests Recovered: The Null Model HW4 out · HW3 due Thu 2/18
2/19 The Non-Parametric Bootstrap: Two Nulls, Two p-Values
5 2/22 The Bootstrap in Practice: Intervals, and Where It Breaks HW5 out Quiz 2
2/24 Bayes: The Same Likelihood, With a Distribution on the Parameters O’Hagan (2004) HW4 due Thu 2/25
2/26 Bayesian Computation: MCMC
6 3/01 MCMC, Continued; the Week-3 Model Fit Both Ways HW6 out MP1 out
3/03 GLMs and Maximum Entropy: Why That Distribution HW5 due Thu 3/04
3/05 GLMs, Continued Quiz 3
7 3/08 Poisson and Count Data; Log-Transform vs. GLM HW7 out
3/10 Extremes I: Block Maxima and the GEV Arns et al. (2013) HW6 due Thu 3/11
3/12 Peaks Over Threshold
8 3/15 Threshold Selection and Declustering
3/17 Extremes II: Nonstationarity MP1 due
3/19 Small Samples and Priors Quiz 4
9 3/22 Where the Bootstrap Itself Becomes Fragile Kyselý (2008)
3/24 Time Series and Autocorrelation Chandler & Wheater (2002) HW7 due Thu 3/25
3/26 Time Series Inference; the Block Bootstrap Proposal due
3/29 Spring Break
3/31 Spring Break
4/02 Spring Break
10 4/05 Model Evaluation I: Predictive Accuracy Roberts et al. (2017) HW8 out MP2 out
4/07 Cross-Validation
4/09 Why Naive k-Fold Fails on Environmental Data Quiz 5
11 4/12 Model Evaluation II: Entropy Recalled; KL Divergence HW9 out
4/14 Deviance HW8 due Thu 4/15
4/16 AIC
12 4/19 Model Evaluation III: WAIC and DIC Anderson & Burnham (2002)
4/21 BIC — and What It Actually Targets
4/23 The Contrast: Predictive Accuracy vs. Marginal Likelihood Check-in due
13 4/26 Missing Data Mechanisms
4/28 Missing Data, Continued HW9 due Thu 4/29
4/30 Multiple Imputation
14 5/03 Content Overflow / Review Quiz 6
5/05 Content Overflow / Review
5/07 Mixture Models and Synthesis (contingent) MP2 due
15 5/10 Class Wrap-Up