- Content (): Readings, slides, and recorded lectures for the topic.
- Labs (): Annotated R code and supplementary information for lab session.
- Problem Set (): Instructions for problem sets. We’ll work on these in class. Due by 11:59 AM (1 minute before class) of the next class.
- Quiz (): Lesson quizzes via Canvas to test chapter reading and lectures. Due by 11:59 AM (1 minute before class) on the day they’re listed.
Jan 10
(Class 1) | Intro to the course / R | | | | |
| Jan 17 | No class (MLK Holiday) | | | | |
Jan 24
(Class 2) | Models & Bayesian Updating | | | | |
Jan 31
(Class 3) | Basic Regression | | | | |
Feb 7
(Class 4) | Not-so-basic Regression | | | | |
Feb 14
(Class 5) | Project Check-in 1 Presentation | | | | |
Feb 21
(Class 6) | Spurious correlations | | | | |
Feb 28
(Class 7) | Model comparison / MCMC | | | | |
| Mar 7 | No class (Spring Break) | | | | |
Mar 14
(Class 8) | GLM | | | | |
Mar 21
(Class 9) | Count GLMs | | | | |
Mar 28
(Class 10) | Guest Lecture | | | | |
Apr 4
(Class 11) | Multilevel Models | | | | |
Apr 11
(Class 12) | Gaussian Processes | | | | |
Apr 18
(Class 13) | Exam | | | | |
Apr 25
(Class 14) | Measurement Error & Missing Data | | | | |
May 2
(Class 15) | Beyond GLMs | | | | |
May 9
(Final Exam) | Project presentations | | | | |