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)
