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
Aux. lessons (optional): Thursdays, 8:30-10:00 pm, 328 SFL
TA OHs: Thursdays, 7:00-8:30 pm, 328 SFL, except Thanksgiving

Saturdays, 1:00-2:30 pm, 328 SFL, except Thanksgiving weekend
JB's OHs: 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 09/26) Introduction to Python / Exploratory data analysis (data set)
Lecture 1: (W 09/28) Probability, Bayes's theorem and the logic of science
Lab 2: (M 10/03) Managing data sets / Defining parameters and estimating them (data set) [tutorial exercise, data set, solutions]
Lecture 2: (W 10/05) An example of Bayesian parameter estimation: The mean and variance from repeated measurements
Lab 3: (M 10/10) Parameter estimation by optimization (data set) / *Probability distributions and their meanings [tutorial exercise, solutions]
Lecture 3: (W 10/12) Markov chain Monte Carlo
Lab 4: (M 10/17) Maximum likelihood estimation / Parameter estimation by Markov chain Monte Carlo (data set) [tutorial exercise, solutions]
Lecture 4: (W 10/19) Model selection
Lab 5: (M 10/24) Credible regions / Outliers (data set) [tutorial exercise]
Lecture 5: (W 10/26) Parallel tempering Markov chain Monte Carlo
Lab 6: (M 10/31) Model selection / PTMCMC (data sets 1, 2) [tutorial exercise]
Lecture 6: (W 11/02) Frequentist methods
Lab 7: (M 11/07) Hacker statistics with frequentist methods / Dancing statistics (data set 1, ) [tutorial exercise]
Lecture 7: (W 11/09) Introduction to images
Lab 8: (M 11/14) Time series / Extracting information from images* (data set 1, data set 2) [tutorial exercise]
Lecture 8: (W 11/16) Hierarchical models
Lab 9: (M 11/21) Basic filtering and thresholding / Segmentation (data set) [tutorial exercise]
Lecture 9: (W 11/23) Good data storage and sharing practices (guest lecture by Tom Morell)
Lab 10: (M 11/28) Colocalization (data set) / Data validation (files for in-class instruction) [tutorial exercise]
Lecture 10: (W 12/01) Course recap and wrap-up

Auxiliary lesson schedule (tentative)

Auxiliary are on Thursdays in 328 SFL, except for the end of the term. They cover auxiliary topics and are completely optional. The schedule below is tentative and may change based on student and instructor/TA interest.

Aux 1: (Th 09/29) Review of concepts in probability (HK)
Aux 2: (Th 10/06) Interactive plotting with Bokeh (JB) [data sets: 1, 2, 3, 4, 5]
Aux 3: (Th 10/13) Discussion on priors (JB) [data set]
Aux 4: (Th 11/20) Principle component analysis (HK) [data set]
Aux 5: (Th 10/27) Kernel density estimation (GC) [data sets 1, 2, 3]
Aux 6: (Th 11/03) K-means clustering (PQ) [data set 1, data set 2]
Aux 7: (Th 11/10) Support vector machines (PQ/JA)
Aux 8: (Th 11/17) t-distributed stochastic neighbor embedding (HK) [data set 1, data set 2]
Aux 9: (Th 12/08) Advanced segmentation: watershed algorithms (GC) Note special start time of 7pm. (data set)