For convenience, below are links to course documents. Timing and topics of recitation lessons are tentative and subject to change.

Tutorials

Tutorial 0a: [Before first class] Setting up a Python environment for scientific computing
Tutorial 0b: [Before first homework] Introduction to Jupyter notebooks
Tutorial 0c: [Before first homework] Introduction to LaTeX
Tutorial 0d: [Before first homework] A sample homework problem and solution
Tutorial 1a: [10/01] Dataframes (data set)
Tutorial 1b: [10/01] Plotting (data set)
Tutorial 2a: [10/08] Tidy data and split-apply-combine (data set)
Tutorial 2b: [10/08] Exploratory data analysis (data set 1, data set 2)
T2 exercise: [10/08] (data set), solution
Tutorial 3a: [10/15] Data validation (data set 1, data set 2)
Tutorial 3b: [10/15] Extracting information from images (data set)
Tutorial 3c: [10/15] Probability distributions and their meanings
T3 exercise: [10/15] solution
Tutorial 4a: [10/22] Image segmentation I (data set)
Tutorial 4b: [10/22] Image segmentation II (data set)
T4 exercise: [10/22] solution
Tutorial 5a: [10/29] Generative models and random number generation (data set)
Tutorial 5b: [10/29] Resampling methods (data set)
T5 exercise: [10/29] solution
Tutorial 6a: [11/05] Construction of generative models/prior predictive checks
Tutorial 6b: [11/05] Brute force posterior evaluation and parameter estimation by optimization
T6 exercise: [11/05]
Tutorial 7a: [11/12] Parameter estimation by Markov chain Monte Carlo
Tutorial 7b: [11/12] Display of MCMC results
T7 exercise: [11/12]
Tutorial 8a: [11/19] MCMC diagnostics
Tutorial 8b: [11/19] Hierarchical models and noncentered distributions
T8 exercise: [11/19]
Tutorial 9a: [11/26] Posterior predictive checks
Tutorial 9b: [11/26] Model comparison
Tutorial 10a: [12/03] Outliers
Tutorial 10b: [12/03] Simulation-based calibration
T10 exercise: [12/03]

Lecture notes

Lec 1: (W 10/03) Introduction to probability
Lec 2: (W 10/10) Good data storage and sharing practices (guest lecture from Tom Morrell)
Lec 3: (W 10/17) Introduction to images (data set)
Lec 4: (W 10/24) Confidence intervals, hypothesis testing, and nonparametric frequentist methods
Lec 5: (W 10/31) Introduction to Bayesian modeling
Lec 6: (W 11/07) Markov chain Monte Carlo
Lec 7: (W 11/14) Hierarchical models (notebook)
Lec 8: (W 11/21) Model comparison
Lec 9: (W 11/28) Statistical pitfalls (Dance of the p-values, Nonidentifiability)
Lec 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]

Homeworks

HW 1: due 1pm, Oct. 7 (data set) [solutions]
HW 2: due 1pm, Oct. 14 [solutions]
HW 3: due 1pm, Oct. 21 (data set 1, data set 2) [solutions]
HW 4: due 1pm, Oct. 28 (data set 1, data set 2) [solutions]
HW 5: due 1pm, Nov. 4 (data set 1, data set 2, data set 3) [solutions]
HW 6: due 1pm, Nov. 18 [solutions]
HW 7: due 10am, Nov. 21 [solutions]
HW 8: due 1pm, Dec. 2 [solutions]
HW 9: due 1pm, Dec. 11 [data set, results from HW4 (only use if your own results for HW4 are unusable)] [solutions]
HW 10: due 1pm, Dec. 14
HW 11: due 1pm, Dec. 14 [solutions]