- Lab 0
- Complete Tutorial 0: Setting up a Python distribution for scientific computing before our first meeting.
- Lab 1
- (M 09/29) Introduction to Python / Loading and displaying data (data set)
- Lecture 1
- (W 10/01) Bayes's theorem, probability and the logic of science, intro to parameter estimation
- Lab 2
- (M 10/06) Managing data sets / Defining parameters and estimating them (data set)
- Lecture 2
- (W 10/08) Parameter estimation
- Lab 3
- (M 10/13) Model generation and checking / Linear regression (data)
- Lecture 3
- (W 10/15) Model selection
- Lab
- (M 10/20) Cancelled due to instructor illness
- Lecture 4
- (W 10/22) Intro to Markov chain Monte Carlo
- Lab 4
- (M 10/27) Nonlinear regression / Model selection (data set 1, data set 2)
- Lecture 5
- (W 10/29) Probability distributions and their meaning
- Lab 5
- (M 11/03) Outlier detection / Data smoothing ( data set)
- Lecture 6
- (W 11/05) Smoothing methods and non-Gaussian likelihoods
- Lab 6
- (M 11/10) Prepping images / A case study in FRAP (data set sent via Dropbox)
- Lecture 7
- (W 11/12) Guest lecture: Jin Park, Elowitz Lab
- Lab 7
- (M 11/17) Basic filtering and thresholding / Segmentation (data sent via Dropbox)
- Lecture 8
- (W 11/19) Error bars and model selection from MCMC calculations
- Lab 8
- (M 11/24) Particle tracking / Cross correlation and PIV (data set)
- Lecture 9
- (W 11/26) Error propagation / Overview of data analysis tools
- Lab 9
- (M 12/01) Colocalization (data set)
- Lecture 10
- (W 12/03) Course recap and wrap-up