Following is a tentative course schedule, which is subject to change as the course progresses. Links to all tutorials will be posted when available. Lab sessions marked with an asterisk feature lecture material.
Weekly activities
- Lab
- Section 1: Mondays, 1:00-4:00 pm, 200 Broad
Section 2: Mondays, 7:00-10:00 pm, 200 Broad - Lecture
- Wednesdays, 10:00-10:50 am, 100 Broad
- TA sessions
- Recitation: Thursdays, 7:00-8:30 pm, 328 SFL, except Thanksgiving
- Homework help: Thursdays, 8:30-10:00 pm, 328 SFL, except Thanksgiving
- Office hours
- TAs: Saturdays, 1:00-2:30 pm, 328 SFL, except Nov. 24
- JB: Fridays, 2:30-4:00 pm, 100 Broad, except Nov. 16 and Nov. 23
Tutorial and lecture schedule
- Lab 0
- (before first
class) Setting up a Python distribution for scientific
computing
(before first homework) Introduction to Jupyter notebooks
(before first homework) Introduction to LaTeX
(before first homework) A sample homework problem and solution - Lab 1
- (M 10/01) Data frames / Plotting (data set 1, data set 2)
- Lecture 1
- (W 10/03) Welcome and introduction to probability
- Lab 2
- (M 10/08) Tidy data and split-apply-combine / Exploratory data analysis (data set 1, data set 2, data set 3) [exercise (data set), solutions]
- Lecture 2
- (W 10/10) Good data storage and sharing practices (guest lecture by Tom Morrell)
- Lab 3
- (M 10/15) Data validation / Extracting info from images / Probability distributions and their meanings (data set 1, data set 2, data set 3) [exercise, solution]
- Lecture 3
- (W 10/17) Introduction to images (data set)
- Lab 4
- (M 10/22) Image segmentation I (data set) / Image segmentation II (data set) [exercise, solution]
- Lecture 4
- (W 10/24) Confidence intervals, hypothesis testing, and nonparametric frequentist methods
- Lab 5
- (M 10/29) Generative models and random number generation / Resampling methods (data set 1, data set 2) [exercise, solution]
- Lecture 5
- (W 10/31) Introduction to Bayesian modeling
- Lab 6
- (M 11/05) Construction of generative models and prior predictive checks / Brute force posterior evaluation and parameter estimation by optimization [exercise]
- Lecture 6
- (W 11/07) Markov chain Monte Carlo
- Lab 7
- (M 11/12) Parameter estimation by Markov chain Monte Carlo / Display of MCMC results [exercise]
- Lecture 7
- (W 11/14) Hierarchical models (notebook)
- Lab 8
- (M 11/19) MCMC diagnostics / Hierarchical models and noncentered distributions [exercise]
- Lecture 8
- (W 11/21) Model comparison
- Lab 9
- (M 11/26) Posterior predictive checks / Model comparison
- Lecture 9
- (W 11/28) Statistical pitfalls (Dance of the p-values, Nonidentifiability)
- Lab 10
- (M 12/03) Outliers / Simulation-based calibration [exercise]
- Lecture 10
- (W 12/05) Course recap and wrap-up
Recitation lessons
- Rec 1
- (Th 10/04) Git/GitHub tips (JW)
- Rec 2
- (Th 10/11) The importance of style (JC)
- Rec 3
- (Th 10/18) Review of probability (CS)
- Rec 4
- (Th 10/25) Cloud computing with AWS (post-setup instructions for EC2 use) (SM and JW)
- Rec 5
- (Th 11/01) Colocalization (SM)
- Rec 6
- (Th 11/08) Review of generative modeling [solutions]
- Rec 7
- (Th 11/15) Revisiting Bayesian model building (JW and JC) [solutions]
- Rec 8
- (Th 11/29) Review of hierarchical modeling [solution]