Links to all tutorials, auxiliary lessons, and lecture notes are available on the schedule page, but they will be listed here for convenient reference.

Tutorials marked with an asterisk are available to Caltech personnel only. This is because they contain unpublished information that the researchers who generously supplied the data would prefer be shared in their own publication. Apologies to non-Caltech people who may otherwise find these tutorials useful.

Similarly, many of the data sets are also only available to Caltech personnel. This is because many of them are unpublished and/or being used for ongoing research.

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: [09/28] Introduction to Python
Tutorial 1b: [09/28] Exploratory data analysis (data set)
Tutorial 2a: [10/03] Managing data sets
Tutorial 2b: [10/03] Defining parameters and estimating them (data set)
T2 exercise: [10/03] solutions (data set)
Tutorial 3a: [10/10] Parameter estimation by optimization (data set)
Tutorial 3b: [10/10] Probability distributions and their stories
T3 exercise: [10/10] solutions
Tutorial 4a: [10/17] Mamimum likelihood estimation (data set)
Tutorial 4b: [10/17] Parameter estimation with Markov chain Monte Carlo
T4 exercise: [10/17] solutions
Tutorial 5a: [10/24] Credible regions
Tutorial 5b: [10/24] Outliers (data set)
T5 exercise: [10/24]
Tutorial 6a: [10/31] Model selection
Tutorial 6b: [10/31] Parallel tempering Markov chain Monte Carlo (data sets 1, 2)
T6 exercise: [10/31]
Tutorial 7a: [11/07] Hacker stats and frequentist methods (data set)
Tutorial 7b: [11/07] Dancing statistics (data set)
T7 exercise: [11/07]
Tutorial 8a: [11/14] Time series and data smoothing (data set)
Tutorial 8b*: [11/14] Extracting data from images (data set)
T8 exercise: [11/14]
Tutorial 9a*: [11/21] Basic filtering and thresholding (data set)
Tutorial 9b*: [11/21] Segmentation
T9 exercise: [11/21]
Tutorial 10a: [11/28] Colocalization (data set)
Tutorial 10b: [11/28] Data validation (files for in-class instruction)
T10 exercise: [11/28]

Lecture notes

Lec 1: (W 09/28) Probability, Bayes's theorem and the logic of science
Lec 2: (W 10/05) An example of Bayesian parameter estimation: The mean and variance from repeated measurements
Lec 3: (W 10/12) Markov chain Monte Carlo
Lec 4: (W 10/19) Model selection
Lec 5: (W 10/26) Parallel tempering Markov chain Monte Carlo
Lec 6: (W 11/02) Frequentist methods
Lec 7: (W 11/09) Intro to images
Lec 8: (W 11/16) Hierarchical models

Auxiliary lessons

Aux 1: (Th 09/29) Review of concepts in probability (HK)
Aux 2: (Th 10/06) Introduction to Bokeh for interactive plotting (JB) [data sets: 1, 2, 3, 4, 5]
Aux 3: (Th 10/13) Discussion on priors (JB) [data set]
Aux 4: (Th 10/20) 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) Watershed algorithm for segmentation (GC) [data set]

Homeworks

HW 1: due 1pm, Oct. 2 (data set) [solutions]
HW 2: due 1pm, Oct. 9 (data set 1, data set 2) [solutions]
HW 3: due 1pm, Oct. 16 (data set 1, data set 2, data set 3, data set 4) [solutions]
HW 4: due 1pm, Oct. 23 [solutions]
HW 5: due 1pm, Nov. 6 (data set 1, data set 2, data set 3) [solutions]
HW 6: due 1pm, Nov. 6 (data set 1, data set 2, data set 3) [solutions]
HW 7: due 1pm, Nov. 13 (data set 1, data set 2, data set 3) [solutions]
HW 8: due 10am, Nov. 23 (data set 1, data set 2) [solutions]
HW 9: due 1pm, Dec. 7 (data set 1, data set 2) [solutions]
HW 10: due 1pm, Dec. 9