Schedule

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
Recitation
Auxiliary material: Thursdays, 7:00-8:30 pm, 328 SFL, except Thanksgiving
Review and homework help: Thursdays, 8:30-10:00 pm, 328 SFL, except Thanksgiving
Office hours
TAs Saturdays, 1:00-2:30 pm, 328 SFL, except Thanksgiving weekend
JB: Fridays, 2:30-4:00 pm, 100 Broad, except Thanksgiving weekend

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 09/25) Introduction to Python / Data frames (data set)
Lecture 1
(W 09/27) Probability, Bayes's theorem and the logic of science
Lab 2
(M 10/02) Tidy data and split-apply-combine / Exploratory data analysis (data set 1, data set 2, data set 3) [exercise, solutions]
Lecture 2
(W 10/04) An example of Bayesian parameter estimation: The mean and variance from repeated measurements
Lab 3
(M 10/09) Data validation / Probability distributions and their meanings (data set 1, data set 2) [exercise, solutions]
Lecture 3
(W 10/11) Constructing Bayesian models
Lab 4
(M 10/16) Parameter estimation by optimization / Maximum likelihood estimation (data set 1, data set 2) [exercise]
Lecture 4
(W 10/18) Markov chain Monte Carlo
Lab 5
(M 10/23) Parameter estimation by Markov chain Monte Carlo / Display of MCMC results (data set 1, data set 2) [exercise]
Lecture 5
(W 10/25) Model comparison
Lab 6
(M 10/30) Outliers / Model comparison [exercise]
Lecture 6
(W 11/01) Frequentist methods
Lab 7
(M 11/06) Hacker statistics with frequentist methods / Dancing statistics [exercise]
Lecture 7
(W 11/08) Introduction to images
Lab 8
(M 11/13) Time series / Extracting information from images [exercise]
Lecture 9
(W 11/15) Hierarchical models (notebook)
Lab 9
(M 11/20) Basic filtering and thresholding / Segmentation [exercise]
Lecture 10
(W 11/22) Good data storage and sharing practices (guest lecture by Tom Morrell)
Lab 10
(M 11/27) Colocalization (data set) [exercise]
Lecture 11
(W 11/29) Course recap and wrap-up

Recitation lessons

Rec 1
(Th 09/28) Review of concepts in probability (CS)
Rec 2
(Th 10/05) Matplotlib and Seaborn (JM)
Rec 3
(Th 10/12) Principle component analysis and t-SNE [data set] (HK)
Rec 4
(Th 10/19) K-means clustering (data set 1, data set 2) (PQ)
Rec 5
(Th 10/26) Support vector machines (JA) and Computing with AWS (PQ)
Rec 6
(Th 11/02) The MCMC Hammer (JB)
Rec 7
(Th 11/09) Parallel tempering Markov chain Monte Carlo (JB) (lecture notes, notebook)
Rec 8
(Th 11/16) Tricks of the MCMC trade (JB)
Rec 9
(Th 11/30) Variational Bayes (HK) and Approximate Bayesian computation (CS)