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
- [09/25] Introduction to Python
- Tutorial 1b
- [09/25] Data frames (data set)
- Tutorial 2a
- [10/02] Tidy data and split-apply-combine (data set)
- Tutorial 2b
- [10/02] Exploratory data analysis (data set 1, data set 2)
- T2 exercise
- [10/02] (data set)
- Tutorial 3a
- [10/09] Data validation (data set 1, data set 2)
- Tutorial 3b
- [10/09] Probability distributions and their meanings
- T3 exercise
- [10/09]
- Tutorial 4a
- [10/16] Parameter estimation by optimization (data set)
- Tutorial 4b
- [10/16] Maximum likelihood estimation (data set)
- T4 exercise
- [10/16]
- Tutorial 5a
- [10/23] Parameter estimation by Markov chain Monte Carlo
- Tutorial 5b
- [10/23] Display of MCMC results (data set 1, data set 2)
- T5 exercise
- [10/23]
- Tutorial 6a
- [10/30] Outliers
- Tutorial 6b
- [10/30] Model comparison
- T6 exercise
- [10/30]
- Tutorial 7a
- [11/06] Hacker stats and frequentist methods
- Tutorial 7b
- [11/06] Dancing statistics
- T7 exercise
- [11/07]
- Tutorial 8a
- [11/13] Time series and data smoothing
- Tutorial 8b
- [11/13] Extracting data from images
- T8 exercise
- [11/13]
- Tutorial 9a
- [11/20] Basic filtering and thresholding (data set)
- Tutorial 9b
- [11/20] Segmentation
- T9 exercise
- [11/20]
- Tutorial 10a
- [11/27] Colocalization (data set)
- T10 exercise
- [11/27]
Lecture notes
- Lec 1
- (W 09/27) Probability, Bayes's theorem and the logic of science
- Lec 2
- (W 10/04) An example of Bayesian parameter estimation: The mean and variance from repeated measurements
- Lec 3
- (W 10/11) Constructing Bayesian models
- Lec 4
- (W 10/18) Markov chain Monte Carlo
- Lec 5
- (W 10/25) Model comparison
- Lec 6
- (W 11/01) Frequentist methods
- Lec 7
- (W 11/08) Intro to images (notebook)
- Lec 8
- (Th 11/09) Parallel tempering MCMC
- Lec 9
- (W 11/15) Hierarchical models (notebook)
- Lec 10
- (W 11/22) Good data storage and sharing practices (guest lecture from Tom Morrell)
- Lec 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 and other clustering methods (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)
Homeworks
- HW 1
- due 1pm, Oct. 1 (data set) [solutions]
- HW 2
- due 1pm, Oct. 8 (data set 1, data set 2) [solutions]
- HW 3
- due 1pm, Oct. 15 [solutions]
- HW 4
- due 1pm, Oct. 22 (data set 1, data set 2, data set 3) [solutions]
- HW 5
- due 1pm, Nov. 5 (data set) [solutions]
- HW 6
- due 1pm, Nov. 5 [solutions]
- HW 7
- due 1pm, Nov. 12
- HW 8
- due 10am, Nov. 22 (data set 1, data set 2)
- HW 9
- due 1pm, Dec. 6 (data set)
- HW 10
- due 1pm, Dec. 8
- HW 11
- due 1pm, Dec. 8